The Factors Affecting the Level of Information Disclosure on Financial Statements in the Industrial Enterprises Listed on Ho Chi Minh Stock Exchange

This research analyzes the factors affecting the level of information disclosure on financial statements in the industrial enterprises listed on Ho Chi Minh stock exchange. Using financial statements of 87 industrial enterprises of the fiscal year 2017, the research shows that there are 6 factors affecting and having a positive relations with the level of information disclosure. These include: the scale of business, Duration of operation, Audit firm reputation, Solvency, Financial leverage and Return on Equity (ROE). The result points to signals that help the State Securities Commission to control better of information disclosure of firms. In addition, the study recommends measures for shareholders, especially those in large companies to strengthen the supervision, control managers in the disclosure of business information

Z-GMOT: Zero-shot Generic Multiple Object Tracking

Despite the significant progress made in recent years, Multi-Object Tracking (MOT) approaches still suffer from several limitations, including their reliance on prior knowledge of tracking targets, which necessitates the costly annotation of large labeled datasets. As a result, existing MOT methods are limited to a small set of predefined categories, and they struggle with unseen objects in the real world. To address these issues, Generic Multiple Object Tracking (GMOT) has been proposed, which requires less prior information about the targets. However, all existing GMOT approaches follow a one-shot paradigm, relying mainly on the initial bounding box and thus struggling to handle variants e.g., viewpoint, lighting, occlusion, scale, and etc. In this paper, we introduce a novel approach to address the limitations of existing MOT and GMOT methods. Specifically, we propose a zero-shot GMOT (Z-GMOT) algorithm that can track never-seen object categories with zero training examples, without the need for predefined categories or an initial bounding box. To achieve this, we propose iGLIP, an improved version of Grounded language-image pretraining (GLIP), which can detect unseen objects while minimizing false positives. We evaluate our Z-GMOT thoroughly on the GMOT-40 dataset, AnimalTrack testset, DanceTrack testset. The results of these evaluations demonstrate a significant improvement over existing methods. For instance, on the GMOT-40 dataset, the Z-GMOT outperforms one-shot GMOT with OC-SORT by 27.79 points HOTA and 44.37 points MOTA. On the AnimalTrack dataset, it surpasses fully-supervised methods with DeepSORT by 12.55 points HOTA and 8.97 points MOTA. To facilitate further research, we will make our code and models publicly available upon acceptance of this paper

Firm-specific News and Anomalies

This study investigates the relation between idiosyncratic volatility and future returns around the firm-specific news announcements in the Korean stock market from July 1995 to June 2018. The excess returns of decile portfolios that are formed by sorting the stocks based on news and non-news idiosyncratic volatility measures. The Fama and French three-factor model is also examined to see whether systematic risk affects news and non-news idiosyncratic volatility profits. The pricing of our news and non-news idiosyncratic volatility are confirmed in the cross-sectional regression using the Fama and MacBeth method. Market beta, size, book to market, momentum, liquidity, and maximum return are controlled to determine robustness. Our empirical evidence suggests that the pricing of the non-news idiosyncratic volatility is more strongly negative compared to the news idiosyncratic volatility, which is contrary to the limited arbitrage explanation for the negative price of the idiosyncratic volatility. We find that the non-news idiosyncratic volatility has a robust negative relation to returns in non-January months. Macro-finance factors drive the conditioned on the missing risk factor hypothesis, the pricing of idiosyncratic volatility. This study contributes to a better understanding of the role of the conditional idiosyncratic volatility in asset pricing. As the Korean stocks provide a fresh sample, our non-U.S. investigation delivers a useful out-of-sample test on the pervasiveness of the non-news volatility effect across the emerging markets

On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors

Recently, there has been a growing focus and interest in applying machine learning (ML) to the field of cybersecurity, particularly in malware detection and prevention. Several research works on malware analysis have been proposed, offering promising results for both academic and practical applications. In these works, the use of Generative Adversarial Networks (GANs) or Reinforcement Learning (RL) can aid malware creators in crafting metamorphic malware that evades antivirus software. In this study, we propose a mutation system to counteract ensemble learning-based detectors by combining GANs and an RL model, overcoming the limitations of the MalGAN model. Our proposed FeaGAN model is built based on MalGAN by incorporating an RL model called the Deep Q-network anti-malware Engines Attacking Framework (DQEAF). The RL model addresses three key challenges in performing adversarial attacks on Windows Portable Executable malware, including format preservation, executability preservation, and maliciousness preservation. In the FeaGAN model, ensemble learning is utilized to enhance the malware detector's evasion ability, with the generated adversarial patterns. The experimental results demonstrate that 100\% of the selected mutant samples preserve the format of executable files, while certain successes in both executability preservation and maliciousness preservation are achieved, reaching a stable success rate

ĐÁNH GIÁ HIỆU QUẢ TẠI CÁC MÔ HÌNH DOANH NGHIỆP THAM GIA QUẢN LÝ RẠN SAN HÔ VÌ MỤC ĐÍCH DU LỊCH SINH THÁI Ở VỊNH NHA TRANG

The models of coral reef management for the development of ecological tourism were conducted under the coordination among the 3 businesses (Khanh Hoa Salanganes Nest Company, Vinpearl Nha Trang and Tri Nguyen Tourism), Institute of Oceanography and Khanh Hoa Department of Natural Resources Environment. The analysis of trends of coral cover, density of reef fishes and big size invertebrates at 3 sites allowed assessing effectiveness of 3 years’ management. The stability of hard coral cover, except the decline at southern Hon Tam due to impacts of the typhoon in Nov., 2017 indicated no increased damage to corals from human activities. However, the dominance of small size fish ( 10 cm in length), the decline of density of larger size fish and the poorness of large size invertebrate showed continuous overexploitation at these managed areas.Mô hình quản lý rạn san hô vì mục đích du lịch sinh thái được thực hiện với sự tham gia của Viện Hải dương học, Sở Tài nguyên và Môi trường Khánh Hòa và 3 doanh nghiệp bao gồm Công ty TNHH Nhà nước MTV Yến Sào, Công ty TNHH Vinpearl Nha Trang và Công ty Du lịch Trí Nguyên. Hiệu quả sau 3 năm quản lý được đánh giá thông quan phân tích xu thế biến động về độ phủ san hô, mật độ cá rạn và sinh vật đáy kích thước lớn. Sự ổn định độ phủ san hô ở khu vực Sau Sao - Vinpearl và Bãi Sạn - Hòn Miếu chứng tỏ san hô không bị suy thoái. Trong khi đó, độ phủ san hô ở Nam Hòn Tằm tăng rõ rệt trong giai đoạn 2015–2017 nhưng giảm đột ngột vào năm 2018 do bão số 12 diễn ra vào tháng 11/2017. Tổng mật độ cá rạn biến động không rõ rệt với ưu thế là nhóm cá có kích thước nhỏ hơn 10 cm, trong khi nhóm cá có kích thước lớn suy giảm đáng kể về mật độ. Mật độ động vật đáy kích thước lớn rất thấp và chủ yếu thuộc về các nhóm không có giá trị kinh tế. Phân tích này chứng tỏ rằng hoạt động quản lý đã ngăn chặn được tác động của con người gây suy thoái san hô nhưng chưa có hiệu quả với hoạt động khai thác nguồn lợi quá mức

Natural and anthropogenic forcing of multi-decadal to centennial scale variability of sea surface temperature in the South China Sea

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Goodkin, N. F., Samanta, D., Bolton, A., Ong, M. R., Hoang, P. K., Vo, S. T., Karnauskas, K. B., & Hughen, K. A. Natural and anthropogenic forcing of multi-decadal to centennial scale variability of sea surface temperature in the South China Sea. Paleoceanography and Paleoclimatology, 36(10), (2021): e2021PA004233, https://doi.org/10.1029/2021PA004233.Four hundred years of reconstructed sea surface temperatures (SSTs) from a coral located off the coast of Vietnam show significant multi-decadal to centennial-scale variability in wet and dry seasons. Wet and dry season SST co-vary significantly at multi-decadal timescales, and the Interdecadal Pacific Oscillation (IPO) explains the majority of variability in both seasons. A newly reconstructed wet season IPO index was compared to other IPO reconstructions, showing significant long-term agreement with varying amplitude of negative IPO signals based on geographic location. Dry season SST also correlates to sea level pressure anomalies and the East Asian Winter Monsoon, although with an inverse relationship from established interannual behavior, as previously seen with an ocean circulation proxy from the same coral. Centennial-scale variability in wet and dry season SST shows 300 years of near simultaneous changes, with an abrupt decoupling of the records around 1900, after which the dry season continues a long-term cooling trend while the wet season remains almost constant. Climate model simulations indicate greenhouse gases as the largest contributor to the decoupling of the wet and dry season SSTs and demonstrate increased heat advection to the western South China Sea in the wet season, potentially disrupting the covariance in seasonal SST.This research was supported by a Singapore National Research Fellowship to N.F. Goodkin (NRFF-2012-03) as administered by the Earth Observatory of Singapore and by a Singapore Ministry of Education Academic Research Fund Tier 2 award to N.F. Goodkin, K.A. Hughen, and K.B. Karnauskas (MOE-2016-T2-1-016). D. Samanta was partially supported by a Singapore Ministry of Education Tier 3 award (MOE2019-T3-1-004)