Whiteflies (Hemiptera: Aleyrodidae) intercepted on plant product imported to South Korea from 2013–2021

Current globalization and technological progress has facilitated and increased the international trade of plant products worldwide and has promoted the long-distance movement of immobile sucking pests such as whiteflies attached on plants. Therefore, being able to compile and update information on intercepted insect pests will help to improve the inspection procedures, to detect, identify and mitigate the damage caused by exotic invasive pests. Records of whiteflies (Hemiptera: Aleyrodidae) intercepted on import plants from 2013 to 2021 in the Pest Information System (PIS) database of South Korea were analyzed. A total of 32 species belonging to 19 genera were intercepted on plants imported into South Korea from 20 countries, mostly located in the Oriental region including China. Brief diagnoses, an identification key and photographs of the 32 species intercepted on agricultural commodities and the countries from which they were detected on plants are given to assist in their identification. In addition, this information provides background data and scientific rationale for decisions regarding the management of whiteflies intercepted at the South Korean ports on imported plant products to prevent the introduction and establishment of exotic whiteflies into South Korea

Pooled Mining Makes Selfish Mining Tricky

Bitcoin, the first successful cryptocurrency, uses the blockchain structure and PoW mechanism to generate blocks. PoW makes an adversary difficult to control the network until she retains over 50\% of the hashrate of the total network. Another cryptocurrency, Ethereum, also uses this mechanism and it did not make problem before. In PoW research, however, several attack strategies are studied. In this paper, we researched selfish mining in the pooled mining environment and found the pooled mining exposes mining information of the block which adversary is mining to the random miners. Using this leaked information, other miners can exploit the selfish miner. At the same time, the adversary loses revenue than when she does honest mining. Because of the existence of our counter method, the adversary with pooled mining cannot do selfish mining easily on Bitcoin or blockchains using PoW

Fast Diffusion Sampler for Inverse Problems by Geometric Decomposition

Diffusion models have shown exceptional performance in solving inverse problems. However, one major limitation is the slow inference time. While faster diffusion samplers have been developed for unconditional sampling, there has been limited research on conditional sampling in the context of inverse problems. In this study, we propose a novel and efficient diffusion sampling strategy that employs the geometric decomposition of diffusion sampling. Specifically, we discover that the samples generated from diffusion models can be decomposed into two orthogonal components: a ``denoised" component obtained by projecting the sample onto the clean data manifold, and a ``noise" component that induces a transition to the next lower-level noisy manifold with the addition of stochastic noise. Furthermore, we prove that, under some conditions on the clean data manifold, the conjugate gradient update for imposing conditioning from the denoised signal belongs to the clean manifold, resulting in a much faster and more accurate diffusion sampling. Our method is applicable regardless of the parameterization and setting (i.e., VE, VP). Notably, we achieve state-of-the-art reconstruction quality on challenging real-world medical inverse imaging problems, including multi-coil MRI reconstruction and 3D CT reconstruction. Moreover, our proposed method achieves more than 80 times faster inference time than the previous state-of-the-art method.Comment: 21 page

Countering Block Withholding Attack Effciently

Bitcoin, well-known cryptocurrency, selected Poof-of-Work (PoW) for its security. PoW mechanism incentivizes participants and deters attacks on the network. Bitcoin seems to have operated the stable distributed network with PoW until now. Researchers found, however, some vulnerabilities in PoW such as selfish mining, block withholding attack, and so on. Especially, after Rosenfeld suggested block withholding attack and Eyal made this attack practical, many variants and countermeasures have been proposed. Most countermeasures, however, were accompanied by changes in the mining algorithm to make the attack impossible, which lowered the practical adaptability. In this paper, we propose a countermeasure to prevent block withholding attack effectively. Mining pools can adapt our method without changing their mining environment

Domain Adaptive Video Semantic Segmentation via Cross-Domain Moving Object Mixing

The network trained for domain adaptation is prone to bias toward the easy-to-transfer classes. Since the ground truth label on the target domain is unavailable during training, the bias problem leads to skewed predictions, forgetting to predict hard-to-transfer classes. To address this problem, we propose Cross-domain Moving Object Mixing (CMOM) that cuts several objects, including hard-to-transfer classes, in the source domain video clip and pastes them into the target domain video clip. Unlike image-level domain adaptation, the temporal context should be maintained to mix moving objects in two different videos. Therefore, we design CMOM to mix with consecutive video frames, so that unrealistic movements are not occurring. We additionally propose Feature Alignment with Temporal Context (FATC) to enhance target domain feature discriminability. FATC exploits the robust source domain features, which are trained with ground truth labels, to learn discriminative target domain features in an unsupervised manner by filtering unreliable predictions with temporal consensus. We demonstrate the effectiveness of the proposed approaches through extensive experiments. In particular, our model reaches mIoU of 53.81% on VIPER to Cityscapes-Seq benchmark and mIoU of 56.31% on SYNTHIA-Seq to Cityscapes-Seq benchmark, surpassing the state-of-the-art methods by large margins.Comment: Accepted to WACV 202

Detective Mining: Selfish Mining Becomes Unrealistic under Mining Pool Environment

One of Bitcoin’s core security guarantees is that, for an attacker to be able to successfully interfere with the Bitcoin network and reverse transactions, they need to control 51% of total hash power. Eyal et al., however, significantly reduces Bitcoin’s security guarantee by introducing another type of attack, called Selfish Mining . The key idea behind selfish mining is for a miner to keep its discovered blocks private, thereby intentionally forking the chain. As a result of a selfish mining attack, even a miner with 25% of the computation power can bias the agreed chain with its blocks. After Eyal\u27s original paper, the concept of selfish mining has been actively studied within the Bitcoin community for several years. This paper studies a fundamental problem regarding the selfish mining strategy under the existence of mining pools. For this, we propose a new attack strategy, called Detective Mining , and show that selfish mining pool is not profitable anymore when other miners use our strategy

Short Selling Attack: A Self-Destructive But Profitable 51% Attack On PoS Blockchains

There have been several 51% attacks on Proof-of-Work (PoW) blockchains recently, including Verge and GameCredits, but the most noteworthy has been the attack that saw hackers make off with up to $18 million after a successful double spend was executed on the Bitcoin Gold network. For this reason, the Proof-of-Stake (PoS) algorithm, which already has advantages of energy efficiency and throughput, is attracting attention as an alternative to the PoW algorithm. With a PoS, the attacker needs to obtain 51% of the cryptocurrency to carry out a 51% attack. But unlike PoW, attacker in a PoS system is highly discouraged from launching 51% attack because he would have to risk losing his entire stake amount to do so. Moreover, even if a 51% attack succeeds, the value of PoS-based cryptocurrency will fall, and the attacker with the most stake will eventually lose the most. In this paper, we try to derive the results that go against these conventional myths. Despite of the significant depreciation of cryptocurrency, our method can make a profit from a 51% attack on the PoS blockchains using the traditional stock market\u27s short selling (or shorting) concept. Our findings are an example to show that the conventional myth that a destructive attack that destroys the blockchain ecosystem totally will not occur because it is fundamentally unprofitable to the attacker itself may be wrong

Word2vec-based latent semantic analysis (W2V-LSA) for topic modeling: A study on blockchain technology trend analysis

Blockchain has become one of the core technologies in Industry 4.0. To help decision-makers establish action plans based on blockchain, it is an urgent task to analyze trends in blockchain technology. However, most of existing studies on blockchain trend analysis are based on effort demanding full-text investigation or traditional bibliometric methods whose study scope is limited to a frequency-based statistical analysis. Therefore, in this paper, we propose a new topic modeling method called Word2vec-based Latent Semantic Analysis (W2V-LSA), which is based on Word2vec and Spherical k-means clustering to better capture and represent the context of a corpus. We then used W2V-LSA to perform an annual trend analysis of blockchain research by country and time for 231 abstracts of blockchain-related papers published over the past five years. The performance of the proposed algorithm was compared to Probabilistic LSA, one of the common topic modeling techniques. The experimental results confirmed the usefulness of W2V-LSA in terms of the accuracy and diversity of topics by quantitative and qualitative evaluation. The proposed method can be a competitive alternative for better topic modeling to provide direction for future research in technology trend analysis and it is applicable to various expert systems related to text mining. (C) 2020 The Authors. Published by Elsevier Ltd