WILLINGNESS TO PAY FOR PUBLIC ECOTOURISM SERVICES IN MALAYSIA

The main focus of this study is to determine the attributes of willingness-to-pay (WTP) of the general public towards the entrance fee for using services in the Public Ecotourism Organization. Contingent Valuation Method is used to estimate the value of non-market good by adopting WTP approach. WTP is the maximum amount consumers are prepared to pay for a good or service and to enjoy recreational facilities. It measures whether an individual is willing to forego their income in order to obtain more goods and better services, and WTP is typically used for non-market goods. This study adopted questionnaires survey to examine the perception on the willingness to pay by visitors for the fees charged by the authorities for the services they provided. 100 local and international respondents among visitors involved in the study. The findings showed that National Park can give a new experience to visitors with beautiful natural landscape. However, the respondents perceived that road linkages of National Park are not proper and fee charged for boat services a bit too high. While, National Zoo is visited mostly to spend time and holiday with family due to attractive wildlife shows available daily. The authority however, needs to improve on hygienic aspect and perhaps to lower down the entrance fees. The attractiveness and shortcoming of the National Park and National Zoo are identified in order to suggest for improvements of services to public. As the economic growth, people will demand for better services and facilities or else willingness to pay will be affected. Thus, it is important for the government organizations to upgrade their services and facilities over time to fulfill the needs of the people

Domain Conditioned Adaptation Network

Tremendous research efforts have been made to thrive deep domain adaptation (DA) by seeking domain-invariant features. Most existing deep DA models only focus on aligning feature representations of task-specific layers across domains while integrating a totally shared convolutional architecture for source and target. However, we argue that such strongly-shared convolutional layers might be harmful for domain-specific feature learning when source and target data distribution differs to a large extent. In this paper, we relax a shared-convnets assumption made by previous DA methods and propose a Domain Conditioned Adaptation Network (DCAN), which aims to excite distinct convolutional channels with a domain conditioned channel attention mechanism. As a result, the critical low-level domain-dependent knowledge could be explored appropriately. As far as we know, this is the first work to explore the domain-wise convolutional channel activation for deep DA networks. Moreover, to effectively align high-level feature distributions across two domains, we further deploy domain conditioned feature correction blocks after task-specific layers, which will explicitly correct the domain discrepancy. Extensive experiments on three cross-domain benchmarks demonstrate the proposed approach outperforms existing methods by a large margin, especially on very tough cross-domain learning tasks.Comment: Accepted by AAAI 202

South Sudan: The Birth of an Economy

We discuss the birth of a new economy in a society that has only recently emerged from a 22-year-long civil war. The pace of growth so far has been fast but uneven. We find that aid and oil money are flowing rapidly into certain sectors, while other employment-generating areas of the economy, particularly agriculture, have barely changed their centuries-old ways. As a result, the recent windfall of wealth has yet to translate into tangible development benefits for the majority of the population. In order to achieve growth in these other sectors, there is a need for more innovation in both government policy and business strategy

Exploring Decision-based Black-box Attacks on Face Forgery Detection

Face forgery generation technologies generate vivid faces, which have raised public concerns about security and privacy. Many intelligent systems, such as electronic payment and identity verification, rely on face forgery detection. Although face forgery detection has successfully distinguished fake faces, recent studies have demonstrated that face forgery detectors are very vulnerable to adversarial examples. Meanwhile, existing attacks rely on network architectures or training datasets instead of the predicted labels, which leads to a gap in attacking deployed applications. To narrow this gap, we first explore the decision-based attacks on face forgery detection. However, applying existing decision-based attacks directly suffers from perturbation initialization failure and low image quality. First, we propose cross-task perturbation to handle initialization failures by utilizing the high correlation of face features on different tasks. Then, inspired by using frequency cues by face forgery detection, we propose the frequency decision-based attack. We add perturbations in the frequency domain and then constrain the visual quality in the spatial domain. Finally, extensive experiments demonstrate that our method achieves state-of-the-art attack performance on FaceForensics++, CelebDF, and industrial APIs, with high query efficiency and guaranteed image quality. Further, the fake faces by our method can pass face forgery detection and face recognition, which exposes the security problems of face forgery detectors

Joint study of genetic regulators for expression traits related to breast cancer

The mRNA expression levels of genes have been shown to have discriminating power for the classification of breast cancer. Studying the heritability of gene expression levels on breast cancer related transcripts can lead to the identification of shared common regulators and inter-regulation patterns, which would be important for dissecting the etiology of breast cancer. We applied multilocus association genome-wide scans to 18 breast cancer related transcripts and combined the results with traditional linkage scans. Regulatory hotspots for these transcripts were identified and some inter-regulation patterns were observed. We also derived evidence on interacting genetic regulatory loci shared by a number of these transcripts. In this paper, by restricting to a set of related genes, we were able to employ a more detailed multilocus approach that evaluates both marginal and interaction association signals at each single-nucleotide polymorphism. Interesting inter-regulation patterns and significant overlaps of genetic regulators between transcripts were observed. Interaction association results returned more expression quantitative trait locus hotspots that are significant