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

Satellite channels with weather-induced impairments

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (leaves 85-87).Title as it appears in MIT commencement exercises program, June 2000: Satellite channels with weather-induced impairments.Bad weather conditions, especially due to rain, cause satellites operating at high frequencies (above 10 GHz) to have significant link attenuation. Usually extra link margins are used to assure link availability. These margins cause inefficient use of precious satellite and terminal power, and unnecessarily limit data throughputs. Efficiency improvements using channel prediction and adaptation over satellite channels with weather-induced impairments are considered in this thesis. First, we consider scintillation and rain attenuation as two dominant factors for signal fading over satellite-earth paths above 10 GHz, and explore physical and mathematical modeling of the two processes. Statistical and spectral analyses of these processes using one or two pole autoregressive (AR) models yield simple linear estimators for the received signal attenuation. Using these estimators, we present results where we can predict the received signal attenuation within ±0.5 dB 1 second ahead and within ± 1.0 dB 4 seconds ahead. For adaptation, we change the signal transmission power, the modulation symbol size, and the code rate adaptively. In particular, we suggest a continuous power control and discrete rate control strategy, through which we build a set of modulation/code states, and discretely change the modulation symbol size and the code rate from state to state. Within each state, continuous power control is implemented. Several examples that use this technique and quantitative analyses of power increase and capacity are provided. The analyses indicate that there is a substantial gain in performance either in capacity and/or power consumption with the adaptive schemes.by Jihwan Patrick Choi.S.M

Financial Distress and Audit Report Lags: An Empirical Study in Korea

This study examines the association between a firm’s financial distress and audit report lags. Through this analysis, we intend to reveal whether auditors consider the clients’ financial distress when performing external audits. This study employs 2,786 firmyear observations from 2011 to 2018. The sample of this study consists of companies listed on the Korea Composite Stock Price Index (KOSPI) and the Korea Securities Dealers Automated Quotation (KOSDAQ). We perform OLS regression analysis to test our hypothesis. The OLS regression analysis is conducted through the SAS and STATA programs. We find that there is a significant and positive association between financial distress and audit report lags. The audit report lags increase as the likelihood of clients’ financial distress increases. The results indicate that audits take different amounts of audit effort when auditors consider financial distress as a business risk when they conduct audits. In other words, we provide evidence that auditors increase the amount of audit effort when the likelihood of clients’ financial distress is high. In the absence of studies on how external auditors respond to audited firms' financial distress, this study analyzes whether external auditors change their audit efforts by assessing the audited firms' financial distress. Second, the empirical result that external auditors actually follow the guidelines related to business risk and financial distress specified in the Korean Auditing Standards supports the effectiveness of the business risk-related regulations specified in the Korean Auditing Standard

Cross Layer Optimization of Wireless Control Links in the Software-Defined LEO Satellite Network

The low earth orbit (LEO) satellite network can benefit from software-defined networking (SDN) by lightening forwarding devices and improving service diversity. In order to apply SDN into the network, however, reliable SDN control links should be associated from satellite gateways to satellites, with the wireless and mobile properties of the network taken into account. Since these characteristics affect both control link association and gateway power allocation, we define a new cross layer SDN control link problem. To the best of our knowledge, this is the first attempt to explore the cross layer control link problem for the software-defined satellite network. A logically centralized SDN control framework constrained by maximum total power is introduced to enhance gateway power efficiency for control link setup. Based on the power control analysis of the problem, a power-efficient control link algorithm is developed, which establishes low latency control links with reduced power consumption. Along with the sensitivity analysis of the proposed control link algorithm, numerical results demonstrate low latency and high reliability of control links established by the algorithm, ultimately suggesting the feasibility, both technical and economical, of the software-defined LEO satellite network. © 2019 BMJ Publishing Group. All rights reserved.1

Performance Evaluation of High-Frequency Mobile Satellite Communications

Communication satellites have a much longer propagation delay than terrestrial communication networks such as cellular or WiFi. In addition, as the carrier frequency moves up, mobile satellite communications show worse performances than the conventional fixed satellite communications. The mobile satellite service (MSS) has not been actively pursued with long latency at high-frequency bands for future applications. In this paper, the adverse impact of long propagation delay in the conventional satellite system is investigated with various user mobility and Doppler-shifted carrier frequency. The satellite network is modeled as a basic delayed feedback channel system and the communication performance is analyzed under delayed channel state information (CSI) for assessing the system feasibility in mobile conditions. The results of performance analysis are provided at high-frequency bands with high-speed user movement, specifically on the outage probability and the channel capacity exploiting three types of channel models: conventional land mobile satellite (LMS) channel models of E. Lutz and C. Loo, and Nakagami fading model. In the circumstance with various user speeds, system performances are evaluated with different propagation delays in the LMS channel models and for line-of-sight (LOS) components in the Nakagami fading. In addition, the conventional models are compared depending on different altitudes for geostationary orbit (GEO), medium earth orbit (MEO), and low earth orbit (LEO) satellites, as well as high-altitude platforms (HAP). © 2019 IEEE.1

Into-TTS : Intonation Template based Prosody Control System

Intonations take an important role in delivering the intention of the speaker. However, current end-to-end TTS systems often fail to model proper intonations. To alleviate this problem, we propose a novel, intuitive method to synthesize speech in different intonations using predefined intonation templates. Prior to the acoustic model training, speech data are automatically grouped into intonation templates by k-means clustering, according to their sentence-final F0 contour. Two proposed modules are added to the end-to-end TTS framework: intonation classifier and intonation encoder. The intonation classifier recommends a suitable intonation template to the given text. The intonation encoder, attached to the text encoder output, synthesizes speech abiding the requested intonation template. Main contributions of our paper are: (a) an easy-to-use intonation control system covering a wide range of users; (b) better performance in wrapping speech in a requested intonation with improved pitch distance and MOS; and (c) feasibility to future integration between TTS and NLP, TTS being able to utilize contextual information. Audio samples are available at https://srtts.github.io/IntoTTS.Comment: Submitted to INTERSPEECH 202

Joint unsupervised and supervised learning for context-aware language identification

Language identification (LID) recognizes the language of a spoken utterance automatically. According to recent studies, LID models trained with an automatic speech recognition (ASR) task perform better than those trained with a LID task only. However, we need additional text labels to train the model to recognize speech, and acquiring the text labels is a cost high. In order to overcome this problem, we propose context-aware language identification using a combination of unsupervised and supervised learning without any text labels. The proposed method learns the context of speech through masked language modeling (MLM) loss and simultaneously trains to determine the language of the utterance with supervised learning loss. The proposed joint learning was found to reduce the error rate by 15.6% compared to the same structure model trained by supervised-only learning on a subset of the VoxLingua107 dataset consisting of sub-three-second utterances in 11 languages.Comment: Accepted by ICASSP 202

An Empirical Study on L2 Accents of Cross-lingual Text-to-Speech Systems via Vowel Space

With the recent developments in cross-lingual Text-to-Speech (TTS) systems, L2 (second-language, or foreign) accent problems arise. Moreover, running a subjective evaluation for such cross-lingual TTS systems is troublesome. The vowel space analysis, which is often utilized to explore various aspects of language including L2 accents, is a great alternative analysis tool. In this study, we apply the vowel space analysis method to explore L2 accents of cross-lingual TTS systems. Through the vowel space analysis, we observe the three followings: a) a parallel architecture (Glow-TTS) is less L2-accented than an auto-regressive one (Tacotron); b) L2 accents are more dominant in non-shared vowels in a language pair; and c) L2 accents of cross-lingual TTS systems share some phenomena with those of human L2 learners. Our findings imply that it is necessary for TTS systems to handle each language pair differently, depending on their linguistic characteristics such as non-shared vowels. They also hint that we can further incorporate linguistics knowledge in developing cross-lingual TTS systems.Comment: Submitted to ICASSP 202