Optimizing the Structure of Mongolian Foreign Trade and the Alternative Policy of Successful Transition

This paper aims to make an alternative development policy which can encourage the foreign trade efficiency. In order to make the policy, the current situation of Mongolian Foreign Trade has been determined and invented the product sectors that have a chance to be developed for the further. In this paper, several methods such as Revealed Comparative Advantage (RCA) method, Product Space Analysis or Monkey and Tree Model, Opportunity Index, and Gravity Model have been used to make analysis. The paper illustrates that firstly, Mongolian Foreign Trade has been becoming more dependent from a single country, a single product and there is no structural shift. In other words, the most part of Mongolian export goods consist of the products that have low sophistication level and low value added, and based on natural resources. Also, the diversification of export goods basket is poor and even no unique products are included in the basket. Therefore, this paper suggests an alternative development policy based on Hidalgo, Ricardo Hausmann, and Bailey Klinger’s policy recommendations and foreign trade policy experience of China whose economic performance was the best in the world last 30 years

The Maternal and Child Health (MCH) Handbook in Mongolia: A Cluster-Randomized, Controlled Trial

Objective To assess the effectiveness of the Maternal and Child Health (MCH) handbook in Mongolia to increase antenatal clinic attendance, and to enhance health-seeking behaviors and other health outcomes. Methods A cluster randomized trial was conducted using the translated MCH handbook in Bulgan, Mongolia to assess its effectiveness in promoting antenatal care attendance. Pregnant women were recruited from 18 randomly allocated districts using shuffled, sealed envelopes. The handbook was implemented immediately for women at their first antenatal visit in the intervention group, and nine months later in the control group. The primary outcome was the number of antenatal care visits of all women residing in the selected districts. Cluster effects were adjusted for using generalized estimation equation. Masking was not possible among care providers, pregnant women and assessors. Findings Nine districts were allocated to the intervention group and the remainder to the control group. The intervention group (253 women) attended antenatal clinics on average 6•9 times, while the control group (248 women) attended 6•2 times. Socioeconomic status affected the frequency of clinic attendance: women of higher socioeconomic status visited antenatal clinics more often. Pregnancy complications were more likely to be detected among women using the handbook. Conclusion The MCH handbook promotes continuous care and showed an increase in antenatal visits among the intervention group. The intervention will help to identify maternal morbidities during pregnancy and promote health-seeking behaviors

Deep convolutional neural network classifier for travel patterns using binary sensors

The early detection of dementia is crucial in independent life style of elderly people. Main intention of this study is to propose device-free non-privacy invasive Deep Convolutional Neural Network classifier (DCNN) for Martino-Saltzman's (MS) travel patterns of elderly people living alone using open dataset collected by binary (passive infrared) sensors. Travel patterns are classified as direct, pacing, lapping, or random according to MS model. MS travel pattern is highly related with person's cognitive state, thus can be used to detect early stage of dementia. The dataset was collected by monitoring a cognitively normal elderly resident by wireless passive infrared sensors for 21 months. First, over 70000 travel episodes are extracted from the dataset and classified by MS travel pattern classifier algorithm for the ground truth. Later, 12000 episodes (3000 for each pattern) were randomly selected from the total episodes to compose training and testing dataset. Finally, DCNN performance was compared with three other classical machine-learning classifiers. The Random Forest and DCNN yielded the best classification accuracies of 94.48% and 97.84%, respectively. Thus, the proposed DCNN classifier can be used to infer dementia through travel pattern matching

Device-Free Non-Privacy Invasive Classification of Elderly Travel Patterns in A Smart House Using PIR Sensors and DCNN

Single resident life style is increasing among the elderly due to the issues of elderly care cost and privacy invasion. However, the single life style cannot be maintained if they have dementia. Thus, the early detection of dementia is crucial. Systems with wearable devices or cameras are not preferred choice for the long-term monitoring. Main intention of this paper is to propose deep convolutional neural network (DCNN) classifier for indoor travel patterns of elderly people living alone using open data set collected by device-free non-privacy invasive binary (passive infrared) sensor data. Travel patterns are classified as direct, pacing, lapping, or random according to Martino– Saltzman (MS) model. MS travel pattern is highly related with person’s cognitive state, and thus can be used to detect early stage of dementia. We have utilized an open data set that was presented by Center for Advanced Studies in Adaptive Systems project, Washington State University. The data set was collected by monitoring a cognitively normal elderly person by wireless passive infrared sensors for 21 months. First, 117 320 travel episodes are extracted from the data set and classified by MS travel pattern classifier algorithm for the ground truth. Later, 12 000 episodes (3000 for each pattern) were randomly selected from the total episodes to compose training and testing data set. Finally, DCNN performance was compared with seven other classical machine-learning classifiers. The random forest and DCNN yielded the best classification accuracies of 94.48% and 97.84%, respectively. Thus, the proposed DCNN classifier can be used to infer dementia through travel pattern matching

FishEye8K: A Benchmark and Dataset for Fisheye Camera Object Detection

With the advance of AI, road object detection has been a prominent topic in computer vision, mostly using perspective cameras. Fisheye lens provides omnidirectional wide coverage for using fewer cameras to monitor road intersections, however with view distortions. To our knowledge, there is no existing open dataset prepared for traffic surveillance on fisheye cameras. This paper introduces an open FishEye8K benchmark dataset for road object detection tasks, which comprises 157K bounding boxes across five classes (Pedestrian, Bike, Car, Bus, and Truck). In addition, we present benchmark results of State-of-The-Art (SoTA) models, including variations of YOLOv5, YOLOR, YOLO7, and YOLOv8. The dataset comprises 8,000 images recorded in 22 videos using 18 fisheye cameras for traffic monitoring in Hsinchu, Taiwan, at resolutions of 1080$\times$1080 and 1280$\times$1280. The data annotation and validation process were arduous and time-consuming, due to the ultra-wide panoramic and hemispherical fisheye camera images with large distortion and numerous road participants, particularly people riding scooters. To avoid bias, frames from a particular camera were assigned to either the training or test sets, maintaining a ratio of about 70:30 for both the number of images and bounding boxes in each class. Experimental results show that YOLOv8 and YOLOR outperform on input sizes 640$\times$640 and 1280$\times$1280, respectively. The dataset will be available on GitHub with PASCAL VOC, MS COCO, and YOLO annotation formats. The FishEye8K benchmark will provide significant contributions to the fisheye video analytics and smart city applications.Comment: CVPR Workshops 202

Towards Privacy-Preserved Aging in Place: A Systematic Review

Owing to progressive population aging, elderly people (aged 65 and above) face challenges in carrying out activities of daily living, while placement of the elderly in a care facility is expensive and mentally taxing for them. Thus, there is a need to develop their own homes into smart homes using new technologies. However, this raises concerns of privacy and data security for users since it can be handled remotely. Hence, with advancing technologies it is important to overcome this challenge using privacy-preserving and non-intrusive models. For this review, 235 articles were scanned from databases, out of which 31 articles pertaining to in-home technologies that assist the elderly in living independently were shortlisted for inclusion. They described the adoption of various methodologies like different sensor-based mechanisms, wearables, camera-based techniques, robots, and machine learning strategies to provide a safe and comfortable environment to the elderly. Recent innovations have rendered these technologies more unobtrusive and privacy-preserving with increasing use of environmental sensors and less use of cameras and other devices that may compromise the privacy of individuals. There is a need to develop a comprehensive system for smart homes which ensures patient safety, privacy, and data security; in addition, robots should be integrated with the existing sensor-based platforms to assist in carrying out daily activities and therapies as required

ArSL21L: Arabic Sign Language Letter Dataset

We present our collected and annotated Arabic Sign Language Letters Dataset (ArSL21L) consisting of 14202 images of 32 letter signs with various back-grounds collected from 50 people.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Safety Helmet detection with Extended Labels 5K images (SHEL5K)

We extended the number of labels in Kaggle’s safety helmet detection dataset, which has 5000 images and 5000 annotations. The original dataset had three classes (person, head and helmet) and a total of 2501 labels. Moreover, the original dataset was incompletely labelled. We added three new labels on the dataset in results, the new labels consists of six classes (helmet, head with helmet, person with helmet, head, person no helmet, and face) and total of 75578 labels.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Safety Helmet detection with Extended Labels Dataset (SHELD)

We extended the number of labels in Kaggle’s safety helmet detection dataset, which has 5000 images and 5000 annotations. The original dataset had three classes (person, head and helmet) and a total of 2501 labels. Moreover, the original dataset was incompletely labelled. We added three new labels on the dataset in results, the new labels consists of six classes (helmet, head with helmet, person with helmet, head, person no helmet, and face) and total of 75578 labels