G-CAME: Gaussian-Class Activation Mapping Explainer for Object Detectors

Nowadays, deep neural networks for object detection in images are very prevalent. However, due to the complexity of these networks, users find it hard to understand why these objects are detected by models. We proposed Gaussian Class Activation Mapping Explainer (G-CAME), which generates a saliency map as the explanation for object detection models. G-CAME can be considered a CAM-based method that uses the activation maps of selected layers combined with the Gaussian kernel to highlight the important regions in the image for the predicted box. Compared with other Region-based methods, G-CAME can transcend time constraints as it takes a very short time to explain an object. We also evaluated our method qualitatively and quantitatively with YOLOX on the MS-COCO 2017 dataset and guided to apply G-CAME into the two-stage Faster-RCNN model.Comment: 10 figure

Trade Liberalization and Development in ICT Sector and its impact on household welfare in Viet Nam

The ICT sector in Viet Nam had not been developed until the 1980s. However, over the last decade of rapid growth, it has had a powerful impact on many aspects of life in this country. Although the ICT sector is still at an early stage of development and lags behind many other countries in the region, the government of Viet Nam made strong commitments to upgrade the nation’s ICT capability and implemented significant reforms in terms of trade and investment liberalization in ICT sector over the last decade.Trade Liberalization, ICT, Household welfare, Viet Nam

A Novel Explainable Artificial Intelligence Model in Image Classification problem

In recent years, artificial intelligence is increasingly being applied widely in many different fields and has a profound and direct impact on human life. Following this is the need to understand the principles of the model making predictions. Since most of the current high-precision models are black boxes, neither the AI scientist nor the end-user deeply understands what's going on inside these models. Therefore, many algorithms are studied for the purpose of explaining AI models, especially those in the problem of image classification in the field of computer vision such as LIME, CAM, GradCAM. However, these algorithms still have limitations such as LIME's long execution time and CAM's confusing interpretation of concreteness and clarity. Therefore, in this paper, we propose a new method called Segmentation - Class Activation Mapping (SeCAM) that combines the advantages of these algorithms above, while at the same time overcoming their disadvantages. We tested this algorithm with various models, including ResNet50, Inception-v3, VGG16 from ImageNet Large Scale Visual Recognition Challenge (ILSVRC) data set. Outstanding results when the algorithm has met all the requirements for a specific explanation in a remarkably concise time.Comment: Published in the Proceedings of FAIC 202

Sustained proliferation in cancer: mechanisms and novel therapeutic targets

Proliferation is an important part of cancer development and progression. This is manifest by altered expression and/or activity of cell cycle related proteins. Constitutive activation of many signal transduction pathways also stimulates cell growth. Early steps in tumor development are associated with a fibrogenic response and the development of a hypoxic environment which favors the survival and proliferation of cancer stem cells. Part of the survival strategy of cancer stem cells may manifested by alterations in cell metabolism. Once tumors appear, growth and metastasis may be supported by overproduction of appropriate hormones (in hormonally dependent cancers), by promoting angiogenesis, by undergoing epithelial to mesenchymal transition, by triggering autophagy, and by taking cues from surrounding stromal cells. A number of natural compounds (e.g., curcumin, resveratrol, indole-3-carbinol, brassinin, sulforaphane, epigallocatechin-3-gallate, genistein, ellagitannins, lycopene and quercetin) have been found to inhibit one or more pathways that contribute to proliferation (e.g., hypoxia inducible factor 1, nuclear factor kappa B, phosphoinositide 3 kinase/Akt, insulin-like growth factor receptor 1, Wnt, cell cycle associated proteins, as well as androgen and estrogen receptor signaling). These data, in combination with bioinformatics analyses, will be very important for identifying signaling pathways and molecular targets that may provide early diagnostic markers and/or critical targets for the development of new drugs or drug combinations that block tumor formation and progression

The capability of terrestrial laser scanning for monitoring the displacement of high-rise buildings

Recently, terrestrial laser scanner (TLS) has been increasingly used to monitor of displacement of high-rise buildings. The main advantages of this technique are time-saving, higher point density, and higher accuracy in comparison with GPS and conventional methods. While TLS is ordinary worldwide, there has been no study of the capability of TLS in monitoring the displacement of high-rise buildings yet in Vietnam. The paper's goal is to build a procedure for displacement monitoring of high-rise buildings and assess the accuracy of TLS in this application. In the experiments, a scanned board with a 60 cm x 60 cm mounted on a moveable monument system is scanned by Faro Focus3D X130. A monitoring procedure using TLS is proposed, including three main stages: site investigation, data acquisition and processing, and displacement determination by the Cloud-to-Cloud method (C2C). As a result, the displacement of the scanned object between epochs is computed. In order to evaluate the accuracy, the estimated displacement using TLS is compared with the real displacement. The accuracy depends on scanning geometry, surface property, and point density conditions. Our results show that the accuracy of the estimated displacement is within ± 2 mm for buildings lower than 50 m of height. Thus, TLS completely meets the accuracy requirements of monitoring displacement in the Vietnam Standards of Engineering Surveying. With such outstanding performance, our workflow of using TLS could be applied to monitor the displacement of high-rise buildings in the reality of geodetic production in Vietnam

Trade liberalization and development in ICT sector and its impact on household welfare in Vietnam

This paper examines the ITC related issues in Viet Nam. The ICT sector in Viet Nam had not been developed until the 1980s. However, over the last decade of rapid growth, it has had a powerful impact on many aspects of life in this country. Although the ICT sector is still at an early stage of development and lags behind many other countries in the region, the government of Viet Nam made strong commitments to upgrade the nation's ICT capability and implemented significant reforms in terms of trade and investment liberalization in ICT sector over the last decade. This commitment has probably been the most important factor in accelerating ICT usage in society and government. It may also have partially contributed to achieve an average annual economic growth rate of 7.6 percent over the period 1991-2006 and reduce the poverty rate from 57 percent of the population in 1993 to less than 20 percent in 2004 (Duc, et al., 2006; VDR, 2005; GSO, 2004). Therefore, the relationship between ICTs development and household welfare in Viet Nam under the dynamic changes over the last decade need to be examined in more details. This is made possible by the availability of four high-quality household surveys3 spanning the period 1993-2004.

Investigation of Methods to Extract Fetal Electrocardiogram from the Mother's Abdominal Signal in Practical Scenarios.

Monitoring of fetal electrocardiogram (fECG) would provide useful information about fetal wellbeing as well as any abnormal development during pregnancy. Recent advances in flexible electronics and wearable technologies have enabled compact devices to acquire personal physiological signals in the home setting, including those of expectant mothers. However, the high noise level in the daily life renders long-entrenched challenges to extract fECG from the combined fetal/maternal ECG signal recorded in the abdominal area of the mother. Thus, an efficient fECG extraction scheme is a dire need. In this work, we intensively explored various extraction algorithms, including template subtraction (TS), independent component analysis (ICA), and extended Kalman filter (EKF) using the data from the PhysioNet 2013 Challenge. Furthermore, the modified data with Gaussian and motion noise added, mimicking a practical scenario, were utilized to examine the performance of algorithms. Finally, we combined different algorithms together, yielding promising results, with the best performance in the F1 score of 92.61% achieved by an algorithm combining ICA and TS. With the data modified by adding different types of noise, the combination of ICA-TS-ICA showed the highest F1 score of 85.4%. It should be noted that these combined approaches required higher computational complexity, including execution time and allocated memory compared with other methods. Owing to comprehensive examination through various evaluation metrics in different extraction algorithms, this study provides insights into the implementation and operation of state-of-the-art fetal and maternal monitoring systems in the era of mobile health