Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images

Semantic segmentation models based on convolutional neural networks (CNNs) have gained much attention in relation to remote sensing and have achieved remarkable performance for the extraction of buildings from high-resolution aerial images. However, the issue of limited generalization for unseen images remains. When there is a domain gap between the training and test datasets, CNN-based segmentation models trained by a training dataset fail to segment buildings for the test dataset. In this paper, we propose segmentation networks based on a domain adaptive transfer attack (DATA) scheme for building extraction from aerial images. The proposed system combines the domain transfer and adversarial attack concepts. Based on the DATA scheme, the distribution of the input images can be shifted to that of the target images while turning images into adversarial examples against a target network. Defending adversarial examples adapted to the target domain can overcome the performance degradation due to the domain gap and increase the robustness of the segmentation model. Cross-dataset experiments and the ablation study are conducted for the three different datasets: the Inria aerial image labeling dataset, the Massachusetts building dataset, and the WHU East Asia dataset. Compared to the performance of the segmentation network without the DATA scheme, the proposed method shows improvements in the overall IoU. Moreover, it is verified that the proposed method outperforms even when compared to feature adaptation (FA) and output space adaptation (OSA).Comment: 11pages, 12 figure

Future development strategies for KODISA journals: overview of 2016 and strategic plans for the future

Purpose – With the rise of the fourth industrial revolution, it has converged with the existing industrial revolution to give shape to increased accessibility of knowledge and information. As a result, it has become easier for scholars to actively pursue and compile research in various fields. This current study aims to focus and assess the current standing of KODISA: the Journal of Distribution Science (JDS), International Journal of Industrial Distribution & Business (IJIDB), the East Asian Journal of Business Management (EAJBM), the Journal of Asian Finance, Economics and Business (JAFEB) in a rapidly evolving era. Novel strategies for creating the future vision of KODISA 2020 will also be examined. Research design, data, and methodology – The current research will analyze published journals of KODISA in order to offer a vision for the KODISA 2020 future. In part 1, this paper will observe the current address of the KODISA journal and its overview of past achievements. Next, part 2 will discuss the activities that will be needed for journals of KODISA, JDS, IJIDB, EAJBM, JAFEB to branch out internationally and significant journals will be statistically analyzed in part 3. The last part 4 will offer strategies for the continued growth of KODISA and visions for KODISA 2020. Results – Among the KODISA publications, IJIDB was second, JDS was 23rd (in economic publications of 54 journals), and EAJBM was 22nd (out of 79 publications in management field journals). This shows the high quality of the KODISA publication journals. According to 2016 publication analysis, JDS, IJIDB, etc. each had 157 publications, 15 publications, 16 publications, and 28 publications. In the case of JDS, it showed an increase of 14% compared to last year. Additionally, JAFEB showed a significant increase of 68%. This shows that compared to other journals, it had a higher rate of paper submission. IJIDB and EAJBM did not show any significant increases. In JDS, it showed many studies related to the distribution, management of distribution, and consumer behavior. In order to increase the status of the KODISA journal to a SCI status, many more international conferences will open to increase its international recognition levels. Second, the systematic functions of the journal will be developed further to increase its stability. Third, future graduate schools will open to foster future potential leaders in this field and build a platform for innovators and leaders. Conclusions – In KODISA, JDS was first published in 1999, and has been registered in SCOPUS February 2017. Other sister publications within the KODISA are preparing for SCOPUS registration as well. KODISA journals will prepare to be an innovative journal for 2020 and the future beyond

Deep Learning-based Synthetic High-Resolution In-Depth Imaging Using an Attachable Dual-element Endoscopic Ultrasound Probe

Endoscopic ultrasound (EUS) imaging has a trade-off between resolution and penetration depth. By considering the in-vivo characteristics of human organs, it is necessary to provide clinicians with appropriate hardware specifications for precise diagnosis. Recently, super-resolution (SR) ultrasound imaging studies, including the SR task in deep learning fields, have been reported for enhancing ultrasound images. However, most of those studies did not consider ultrasound imaging natures, but rather they were conventional SR techniques based on downsampling of ultrasound images. In this study, we propose a novel deep learning-based high-resolution in-depth imaging probe capable of offering low- and high-frequency ultrasound image pairs. We developed an attachable dual-element EUS probe with customized low- and high-frequency ultrasound transducers under small hardware constraints. We also designed a special geared structure to enable the same image plane. The proposed system was evaluated with a wire phantom and a tissue-mimicking phantom. After the evaluation, 442 ultrasound image pairs from the tissue-mimicking phantom were acquired. We then applied several deep learning models to obtain synthetic high-resolution in-depth images, thus demonstrating the feasibility of our approach for clinical unmet needs. Furthermore, we quantitatively and qualitatively analyzed the results to find a suitable deep-learning model for our task. The obtained results demonstrate that our proposed dual-element EUS probe with an image-to-image translation network has the potential to provide synthetic high-frequency ultrasound images deep inside tissues.Comment: 10 pages, 9 figure

Transplantation of Adipose Derived Stromal Cells into the Developing Mouse Eye

Adipose derived stromal cells (ADSCs) were transplanted into a developing mouse eye to investigate the influence of a developing host micro environment on integration and differentiation. Green fluorescent protein-expressing ADSCs were transplanted by intraocular injections. The age of the mouse was in the range of 1 to 10 days postnatal (PN). Survival dates ranged from 7 to 28 post transplantation (DPT), at which time immunohistochemistry was performed. The transplanted ADSCs displayed some morphological differentiations in the host eye. Some cells expressed microtubule associated protein 2 (marker for mature neuron), or glial fibrillary acid protein (marker for glial cell). In addition, some cells integrated into the ganglion cell layer. The integration and differentiation of the transplanted ADSCs in the 5 and 10 PN 7 DPT were better than in the host eye the other age ranges. This study was aimed at demonstrating how the age of host micro environment would influence the differentiation and integration of the transplanted ADSCs. However, it was found that the integration and differentiation into the developing retina were very limited when compared with other stem cells, such as murine brain progenitor cell

Cases of ethical violation in research publications: through editorial decision making process

Purpose – To improve and strengthen existing publication and research ethics, KODISA has identified and presented various cases which have violated publication and research ethics and principles in recent years. The editorial office of KODISA has been providing and continues to provide advice and feedback on publication ethics to researchers during peer review and editorial decision making process. Providing advice and feedback on publication ethics will ensure researchers to have an opportunity to correct their mistakes or make appropriate decisions and avoid any violations in research ethics. The purpose of this paper is to identify different cases of ethical violation in research and inform and educate researchers to avoid any violations in publication and research ethics. Furthermore, this article will demonstrate how KODISA journals identify and penalize ethical violations and strengthens its publication ethics and practices. Research design, data and methodology – This paper examines different types of ethical violation in publication and research ethics. The paper identifies and analyzes all ethical violations in research and combines them into five general categories. Those five general types of ethical violations are thoroughly examined and discussed. Results – Ethical violations of research occur in various forms at regular intervals; in other words, unethical researchers tend to commit different types of ethical violations repeatedly at same time. The five categories of ethical violation in research are as follows: (1) Arbitrary changes or additions in author(s) happen frequently in thesis/dissertation related publications. (2) Self plagiarism, submitting same work or mixture of previous works with or without using proper citations, also occurs frequently, but the most common type of plagiarism is changing the statistical results and using them to present as the results of the empirical analysis; (3) Translation plagiarism, another ethical violation in publication, is difficult to detect but occurs frequently; (4) Fabrication of data or statistical analysis also occurs frequently. KODISA requires authors to submit the results of the empirical analysis of the paper (the output of the statistical program) to prevent this type of ethical violation; (5) Mashup or aggregator plagiarism, submitting a mix of several different works with or without proper citations without alterations, is very difficult to detect, and KODISA journals consider this type of plagiarism as the worst ethical violation. Conclusions – There are some individual cases of ethical violation in research and publication that could not be included in the five categories presented throughout the paper. KODISA and its editorial office should continue to develop, revise, and strengthen their publication ethics, to learn and share different ways to detect any ethical violations in research and publication, to train and educate its editorial members and researchers, and to analyze and share different cases of ethical violations with the scholarly community

Prevalence of Bartonella henselae and Bartonella clarridgeiae in cats and dogs in Korea

Blood, saliva, and nail samples were collected from 54 dogs and 151 cats and analyzed for the presence of Bartonella henselae with a novel nested polymerase chain reaction (PCR) method. Bartonella (B.) henselae was detected in feral cat blood (41.8%), saliva (44.1%), and nail (42.7%) samples. B. henselae was also detected in pet cat blood (33.3%), saliva (43.5%), and nail (29.5%) samples and in pet dog blood (16.6%), saliva (18.5%), and nail (29.6%) samples. Nine samples were infected with B. clarridgeiae and 2 were co-infected with B. henselae and B. clarridgeiae of blood samples of dogs. This report is the first to investigate the prevalence of B. henselae and B. clarridgeiae in dogs and cats in Korea, and suggests that dogs and cats may serve as potential Bartonella reservoirs

An Energy-Efficient Algorithm for Classification of Fall Types Using a Wearable Sensor

Objective: To mitigate damage from falls, it is essential to provide medical attention expeditiously. Many previous studies have focused on detecting falls and have shown that falls can be accurately detected at least in a laboratory setting. However, a very few studies have classified the different types of falls. To this end, in this paper, a novel energy-efficient algorithm that can discriminate the five most common fall types was developed for wearable systems. Methods: A wearable system with an inertial measurement unit sensor was first developed. Then, our novel algorithm, temporal signal angle measurement (TSAM), was used to classify the different types of falls at various sampling frequencies, and the results were compared with those from three different machine learning algorithms. Results: The overall performance of the TSAM and that of the machine learning algorithms were similar. However, the TSAM outperformed the machine learning algorithms at frequencies in the range of 10-20 Hz. As the sampling frequency dropped from 200 to 10Hz, the accuracy of the TSAM ranged from 93.3% to 91.8%. The sensitivity and specificity ranges from 93.3% to 91.8%, and 98.3% to 97.9%, respectively for the same frequency range. Conclusion: Our algorithm can be utilized with energy-efficient wearable devices at low sampling frequencies to classify different types of falls. Significance: Our system can expedite medical assistance in emergency situations caused by falls by providing the necessary information to medical doctors or clinicians.1

Neuropsychological profiles of patients with obsessive-compulsive disorder: early onset versus late onset

In this study, we assess the neuropsychological profiles of both early and late symptom-onset obsessive-compulsive disorder (OCD) patients. The early and late-onset OCD patients are compared to the control group with a series of neuropsychological measurements. The late-onset OCD patients exhibited impaired performance on the immediate and the delayed recall conditions of the Rey-Osterrieth Complex Figure Test (RCFT) and the letter and category fluency of the Controlled Oral Word Association Test (COWA), compared to the normal controls and the early-onset OCD patients. The controls and early-onset OCD patients did not differ on any of the neuropsychological measurements taken in this study. These results suggest that different neurophysiological mechanisms are in play in early and late-onset OCD patients, and age of onset can serve as a potential marker for the subtyping of OCD