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"abstract": "With the widespread application of deep learning systems, the robustness of deep neural networks (DNNs) is received increasing attentions recently. By studying the distribution of neurons outputs in DNN models, we found that the behavior patterns of neurons are different for different kinds of DNNs' inputs, e.g. test cases generated by different adversarial attack techniques. In this paper, we extract the neuron behavior patterns of DNNs under different adversarial attack techniques, use them as the guidance for test case selection. Experimental results show that this method is more efficient than random technology.",
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"abstract": "This paper proposes a neuron drop-out mechanism to control the training paces of mobile devices in federated deep learning. The aim is to accelerate the speed of local training on slow mobile devices with minimal impact on training quality, such that slow mobile devices can catch up with fast devices in each training round to increase the overall training speed. The basic idea is to avoid the computation of some neurons with low activation values (i.e., neuron dropout), and dynamically adjust dropout rates based on the training progress on each mobile device. The paper introduces two techniques for selecting neurons, LSH and Max Heap, and a method for dynamically adjusting dropout rates. It also discusses a few other approaches that can be used to control training paces.",
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"title": "Enhancing Semantic Segmentation of Aerial Images with Inhibitory Neurons",
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"abstract": "In a Convolutional Neural Network, each neuron in the output feature map takes input from the neurons in its receptive field. This receptive field concept plays a vital role in today's deep neural networks. However, inspired by neuro-biological research, it has been proposed to add inhibitory neurons outside the receptive field, which may enhance the performance of neural network models. In this paper, we begin with deep network architectures such as VGG and ResNet, and propose an approach to add lateral inhibition in each output neuron to reduce its impact on its neighbours, both in fine-tuning pre-trained models and training from scratch. Our experiments show that notable improvements upon prior baseline deep models can be achieved. A key feature of our approach is that it is easy to add to baseline models; it can be adopted in any model containing convolution layers, and we demonstrate its value in applications including object recognition and semantic segmentation of aerial images, where we show state-of-the-art result on the Aeroscape dataset. On semantic segmentation tasks, our enhancement shows 17.43% higher mIoU than a single baseline model on a single source (the Aeroscape dataset), 13.43% higher performance than an ensemble model on the same single source, and 7.03% higher than an ensemble model on multiple sources (segmentation datasets). Our experiments illustrate the potential impact of using inhibitory neurons in deep learning models, and they also show better results than the baseline models that have standard convolutional layer.",
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"content": "In a Convolutional Neural Network, each neuron in the output feature map takes input from the neurons in its receptive field. This receptive field concept plays a vital role in today's deep neural networks. However, inspired by neuro-biological research, it has been proposed to add inhibitory neurons outside the receptive field, which may enhance the performance of neural network models. In this paper, we begin with deep network architectures such as VGG and ResNet, and propose an approach to add lateral inhibition in each output neuron to reduce its impact on its neighbours, both in fine-tuning pre-trained models and training from scratch. Our experiments show that notable improvements upon prior baseline deep models can be achieved. A key feature of our approach is that it is easy to add to baseline models; it can be adopted in any model containing convolution layers, and we demonstrate its value in applications including object recognition and semantic segmentation of aerial images, where we show state-of-the-art result on the Aeroscape dataset. On semantic segmentation tasks, our enhancement shows 17.43% higher mIoU than a single baseline model on a single source (the Aeroscape dataset), 13.43% higher performance than an ensemble model on the same single source, and 7.03% higher than an ensemble model on multiple sources (segmentation datasets). Our experiments illustrate the potential impact of using inhibitory neurons in deep learning models, and they also show better results than the baseline models that have standard convolutional layer.",
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"abstract": "Detecting logo frequency and duration in sports videos provides sponsors an effective way to evaluate their advertising efforts. However, general-purposed object detection methods cannot address all the challenges in sports videos. In this paper, we propose a mutual-enhanced approach that can improve the detection of a logo through the information obtained from other simultaneously occurred logos. In a Fast-RCNN-based framework, we first introduce a homogeneity-enhanced re-ranking method by analyzing the characteristics of homogeneous logos in each frame, including type repetition, color consistency, and mutual exclusion. Different from conventional enhance mechanism that improves the weak proposals with the dominant proposals, our mutual method can also enhance the relatively significant proposals with weak proposals. Mutual enhancement is also included in our frame propagation mechanism that improves logo detection by utilizing the continuity of logos across frames. We use a tennis video dataset and an associated logo collection for detection evaluation. Experiments show that the proposed method outperforms existing methods with a higher accuracy.",
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"content": "Detecting logo frequency and duration in sports videos provides sponsors an effective way to evaluate their advertising efforts. However, general-purposed object detection methods cannot address all the challenges in sports videos. In this paper, we propose a mutual-enhanced approach that can improve the detection of a logo through the information obtained from other simultaneously occurred logos. In a Fast-RCNN-based framework, we first introduce a homogeneity-enhanced re-ranking method by analyzing the characteristics of homogeneous logos in each frame, including type repetition, color consistency, and mutual exclusion. Different from conventional enhance mechanism that improves the weak proposals with the dominant proposals, our mutual method can also enhance the relatively significant proposals with weak proposals. Mutual enhancement is also included in our frame propagation mechanism that improves logo detection by utilizing the continuity of logos across frames. We use a tennis video dataset and an associated logo collection for detection evaluation. Experiments show that the proposed method outperforms existing methods with a higher accuracy.",
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"abstract": "The exponential growth in video content generated via multiple sources has led to the development of video summarization mechanisms, where smaller summaries are generated from comparatively longer videos. Video summarization plays a significant role in a diverse range of applications including secured surveillance, biometrics, education, sports, news, movies, etc. Several criteria are used for video summarization based on certain events or objects present in the input video for generating respective small summaries. However, audio is a key parameter that may be used to obtain a small summary of a video where some sound or voice is noticed. This article aims to expound on an analysis of audio-based video summarization (ABVS) via data-driven techniques such as a machine or deep learning. We also present a novel framework for ABVS using handcrafted or automatic feature engineering. An evaluation methodology including performance metrics and datasets for assessing the effectiveness of various ABVS approaches is also illustrated. The study reveals several key issues related to ABVS that need further investigation from the active researchers in this active field. One of the critical issues involves exploring effective audio features and the scarcity of benchmark datasets for an audio-based summary generation.",
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"abstract": "Careful selection of aggregate views and some of their most used indexes to materialize in a data warehouse reduces the warehouse query response time as well as warehouse maintenance cost under some storage space constraint. Data warehouses collect and store large amounts of integrated enterprise data from a number of independent data sources over a long period of time. Warehouse data are used for online analytical processing to assist management in making quick and competitive business decisions. Precomputing and storing summary tables (materialized views) reduces the amount of time needed to recompute these views across several source tables in order to answer complex warehouse queries. A data cube is an elegant way for representing aggregate information in a Warehouse and is an n-dimensional view with 2/sup n/ subviews. The paper presents a uniform technique for selecting the subviews of the data cube and their indexes to materialize in order to produce the best resultant benefit to the system in terms of query response time and maintenance cost while satisfying some storage space constraint.",
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"title": "2019 Crypto Valley Conference on Blockchain Technology (CVCBT)",
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"abstract": "In decentralized blockchain systems such as Bitcoin, the miners are rewarded for generating new blocks and processing the transactions. In order to reduce the reward variance, the miners join mining pools where the miners aggregate their computational resources and divide the reward according to their contributions, measured by shares. The miner-based attacks such as Block Withholding (BWH) and Fork After Withholding (FAW) yield unfair reward advantages to the attackers at the expense of the other honest miners by having the attackers withhold blocks while still submitting the shares and pretending to contribute to the victim pool. This paper introduces Anti-Withholding Reward System (AWRS) to prevent FAW and BWH attacks. Implemented at the pool manager (reducing the adoption overhead and supporting backward-compatibility), AWRS deprives the incentive for FAW and BWH attacks by providing greater rewards to the block submissions. AWRS completely disincentivizes the FAW attack and reduces the optimal attacker's behavior to honest mining, making the withholding-based threats irrelevant for rational miners.",
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"content": "In decentralized blockchain systems such as Bitcoin, the miners are rewarded for generating new blocks and processing the transactions. In order to reduce the reward variance, the miners join mining pools where the miners aggregate their computational resources and divide the reward according to their contributions, measured by shares. The miner-based attacks such as Block Withholding (BWH) and Fork After Withholding (FAW) yield unfair reward advantages to the attackers at the expense of the other honest miners by having the attackers withhold blocks while still submitting the shares and pretending to contribute to the victim pool. This paper introduces Anti-Withholding Reward System (AWRS) to prevent FAW and BWH attacks. Implemented at the pool manager (reducing the adoption overhead and supporting backward-compatibility), AWRS deprives the incentive for FAW and BWH attacks by providing greater rewards to the block submissions. AWRS completely disincentivizes the FAW attack and reduces the optimal attacker's behavior to honest mining, making the withholding-based threats irrelevant for rational miners.",
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"proceeding": {
"id": "12OmNwseER8",
"title": "2019 IEEE Symposium on Security and Privacy (SP)",
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"article": {
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"doi": "10.1109/SP.2019.00075",
"title": "Bitcoin vs. Bitcoin Cash: Coexistence or Downfall of Bitcoin Cash?",
"normalizedTitle": "Bitcoin vs. Bitcoin Cash: Coexistence or Downfall of Bitcoin Cash?",
"abstract": "Bitcoin has become the most popular cryptocurrency based on a peer-to-peer network. In Aug. 2017, Bitcoin was split into the original Bitcoin (BTC) and Bitcoin Cash (BCH). Since then, miners have had a choice between BTC and BCH mining because they have compatible proof-of-work algorithms. Therefore, they can freely choose which coin to mine for higher profit, where the profitability depends on both the coin price and mining difficulty. Some miners can immediately switch the coin to mine only when mining difficulty changes because the difficulty changes are more predictable than that for the coin price, and we call this behavior fickle mining. In this paper, we study the effects of fickle mining by modeling a game between two coins. To do this, we consider both fickle miners and some factions (e.g., BITMAIN for BCH mining) that stick to mining one coin to maintain that chain. In this model, we show that fickle mining leads to a Nash equilibrium in which only a faction sticking to its coin mining remains as a loyal miner to the less valued coin (e.g., BCH), where loyal miners refer to those who conduct mining even after coin mining difficulty increases. This situation would cause severe centralization, weakening the security of the coin system. To determine which equilibrium the competing coin systems (e.g., BTC vs. BCH) are moving toward, we traced the historical changes of mining power for BTC and BCH and found that BCH often lacked loyal miners until Nov. 13, 2017, when the difficulty adjustment algorithm of BCH mining was changed. However, the change in difficulty adjustment algorithm of BCH mining led to a state close to the stable coexistence of BTC and BCH. We also demonstrate that the lack of BCH loyal miners may still be reached when a fraction of miners automatically and repeatedly switches to the most profitable coin to mine (i.e., automatic mining). According to our analysis, as of Dec. 2018, loyal miners to BCH would leave if more than about 5% of the total mining capacity for BTC and BCH has engaged in the automatic mining. In addition, we analyze the recent “hash war” between Bitcoin ABC and SV, which confirms our theoretical analysis. Finally, we note that our results can be applied to any competing cryptocurrency systems in which the same hardware (e.g., ASICs or GPUs) can be used for mining. Therefore, our study brings new and important angles in competitive coin markets: a coin can intentionally weaken the security and decentralization level of the other rival coin when mining hardware is shared between them, allowing for automatic mining.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Bitcoin has become the most popular cryptocurrency based on a peer-to-peer network. In Aug. 2017, Bitcoin was split into the original Bitcoin (BTC) and Bitcoin Cash (BCH). Since then, miners have had a choice between BTC and BCH mining because they have compatible proof-of-work algorithms. Therefore, they can freely choose which coin to mine for higher profit, where the profitability depends on both the coin price and mining difficulty. Some miners can immediately switch the coin to mine only when mining difficulty changes because the difficulty changes are more predictable than that for the coin price, and we call this behavior fickle mining. In this paper, we study the effects of fickle mining by modeling a game between two coins. To do this, we consider both fickle miners and some factions (e.g., BITMAIN for BCH mining) that stick to mining one coin to maintain that chain. In this model, we show that fickle mining leads to a Nash equilibrium in which only a faction sticking to its coin mining remains as a loyal miner to the less valued coin (e.g., BCH), where loyal miners refer to those who conduct mining even after coin mining difficulty increases. This situation would cause severe centralization, weakening the security of the coin system. To determine which equilibrium the competing coin systems (e.g., BTC vs. BCH) are moving toward, we traced the historical changes of mining power for BTC and BCH and found that BCH often lacked loyal miners until Nov. 13, 2017, when the difficulty adjustment algorithm of BCH mining was changed. However, the change in difficulty adjustment algorithm of BCH mining led to a state close to the stable coexistence of BTC and BCH. We also demonstrate that the lack of BCH loyal miners may still be reached when a fraction of miners automatically and repeatedly switches to the most profitable coin to mine (i.e., automatic mining). According to our analysis, as of Dec. 2018, loyal miners to BCH would leave if more than about 5% of the total mining capacity for BTC and BCH has engaged in the automatic mining. In addition, we analyze the recent “hash war” between Bitcoin ABC and SV, which confirms our theoretical analysis. Finally, we note that our results can be applied to any competing cryptocurrency systems in which the same hardware (e.g., ASICs or GPUs) can be used for mining. Therefore, our study brings new and important angles in competitive coin markets: a coin can intentionally weaken the security and decentralization level of the other rival coin when mining hardware is shared between them, allowing for automatic mining.",
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"normalizedAbstract": "Bitcoin has become the most popular cryptocurrency based on a peer-to-peer network. In Aug. 2017, Bitcoin was split into the original Bitcoin (BTC) and Bitcoin Cash (BCH). Since then, miners have had a choice between BTC and BCH mining because they have compatible proof-of-work algorithms. Therefore, they can freely choose which coin to mine for higher profit, where the profitability depends on both the coin price and mining difficulty. Some miners can immediately switch the coin to mine only when mining difficulty changes because the difficulty changes are more predictable than that for the coin price, and we call this behavior fickle mining. In this paper, we study the effects of fickle mining by modeling a game between two coins. To do this, we consider both fickle miners and some factions (e.g., BITMAIN for BCH mining) that stick to mining one coin to maintain that chain. In this model, we show that fickle mining leads to a Nash equilibrium in which only a faction sticking to its coin mining remains as a loyal miner to the less valued coin (e.g., BCH), where loyal miners refer to those who conduct mining even after coin mining difficulty increases. This situation would cause severe centralization, weakening the security of the coin system. To determine which equilibrium the competing coin systems (e.g., BTC vs. BCH) are moving toward, we traced the historical changes of mining power for BTC and BCH and found that BCH often lacked loyal miners until Nov. 13, 2017, when the difficulty adjustment algorithm of BCH mining was changed. However, the change in difficulty adjustment algorithm of BCH mining led to a state close to the stable coexistence of BTC and BCH. We also demonstrate that the lack of BCH loyal miners may still be reached when a fraction of miners automatically and repeatedly switches to the most profitable coin to mine (i.e., automatic mining). According to our analysis, as of Dec. 2018, loyal miners to BCH would leave if more than about 5% of the total mining capacity for BTC and BCH has engaged in the automatic mining. In addition, we analyze the recent “hash war” between Bitcoin ABC and SV, which confirms our theoretical analysis. Finally, we note that our results can be applied to any competing cryptocurrency systems in which the same hardware (e.g., ASICs or GPUs) can be used for mining. Therefore, our study brings new and important angles in competitive coin markets: a coin can intentionally weaken the security and decentralization level of the other rival coin when mining hardware is shared between them, allowing for automatic mining.",
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"affiliation": "Sungkyunkwan University",
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"fullName": "Jinwoo Shin",
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"affiliation": "KAIST",
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"title": "2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)",
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"title": "Multi-Miner's Cooperative Evolution Method of Bitcoin Pool Based on Temporal Difference Leaning Method",
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"abstract": "Proof of Work (PoW) is used to provide a consensus mechanism for Bitcoin. In this mechanism, the process of generating a new block in the blockchain is referred to as mining. Such process is intentionally designed to be resource-intensive and time consuming so that the rate of block generation remains steady. A single participant, called a miner usually has limited computation power to produce PoWs. This leads miners to form a mining pool, where miners aggregate their computing power and share the rewards. However, a phenomenon raises in such a mining pool activity, where miners attack each other. Consequently, this results in a decrease in total rewards received from the mining pool. To address the abovementioned problem, we build a multi-miner model for forming a mining pool. We further propose a method to improve the cooperation-probability of miners in the pool by introducing a Zero-Determinant strategy and a Temporal Difference learning method (TD(λ)). Experimental simulation results show that the proposed method can effectively promote the cooperation among miners, therefore, increase the rewards received from the formed mining pool.",
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{
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"content": "Proof of Work (PoW) is used to provide a consensus mechanism for Bitcoin. In this mechanism, the process of generating a new block in the blockchain is referred to as mining. Such process is intentionally designed to be resource-intensive and time consuming so that the rate of block generation remains steady. A single participant, called a miner usually has limited computation power to produce PoWs. This leads miners to form a mining pool, where miners aggregate their computing power and share the rewards. However, a phenomenon raises in such a mining pool activity, where miners attack each other. Consequently, this results in a decrease in total rewards received from the mining pool. To address the abovementioned problem, we build a multi-miner model for forming a mining pool. We further propose a method to improve the cooperation-probability of miners in the pool by introducing a Zero-Determinant strategy and a Temporal Difference learning method (TD(λ)). Experimental simulation results show that the proposed method can effectively promote the cooperation among miners, therefore, increase the rewards received from the formed mining pool.",
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"normalizedAbstract": "Proof of Work (PoW) is used to provide a consensus mechanism for Bitcoin. In this mechanism, the process of generating a new block in the blockchain is referred to as mining. Such process is intentionally designed to be resource-intensive and time consuming so that the rate of block generation remains steady. A single participant, called a miner usually has limited computation power to produce PoWs. This leads miners to form a mining pool, where miners aggregate their computing power and share the rewards. However, a phenomenon raises in such a mining pool activity, where miners attack each other. Consequently, this results in a decrease in total rewards received from the mining pool. To address the abovementioned problem, we build a multi-miner model for forming a mining pool. We further propose a method to improve the cooperation-probability of miners in the pool by introducing a Zero-Determinant strategy and a Temporal Difference learning method (TD(λ)). Experimental simulation results show that the proposed method can effectively promote the cooperation among miners, therefore, increase the rewards received from the formed mining pool.",
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"Data Mining",
"Learning Artificial Intelligence",
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"Consensus Mechanism",
"Block Generation",
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"Po W",
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"Mining Pool Activity",
"Miners Attack",
"Multiminer Model",
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"Multiminer Cooperative Evolution Method",
"Proof Of Work",
"Conferences",
"Internet Of Things",
"Green Computing",
"Social Computing",
"Bitcoin Blockchain Temporal Difference Learning Method Zero Determinant Strategy Block Withholding Attack"
],
"authors": [
{
"affiliation": "Hunan University of Science and Engineering",
"fullName": "Wei Ou",
"givenName": "Wei",
"surname": "Ou",
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{
"affiliation": "Hunan University of Science and Engineering",
"fullName": "Mingwei Deng",
"givenName": "Mingwei",
"surname": "Deng",
"__typename": "ArticleAuthorType"
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{
"affiliation": "Hunan University of Science and Engineering",
"fullName": "Entao Luo",
"givenName": "Entao",
"surname": "Luo",
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},
{
"affiliation": "Carleton University",
"fullName": "Wei Shi",
"givenName": "Wei",
"surname": "Shi",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Napier University Edinburgh",
"fullName": "Zhiyuan Tan",
"givenName": "Zhiyuan",
"surname": "Tan",
"__typename": "ArticleAuthorType"
},
{
"affiliation": "Fordham University",
"fullName": "Md Zakirul Alam Bhuiyan",
"givenName": "Md Zakirul Alam",
"surname": "Bhuiyan",
"__typename": "ArticleAuthorType"
}
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"title": "2019 IEEE International Conference on Blockchain (Blockchain)",
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"doi": "10.1109/Blockchain.2019.00023",
"title": "Bitcoin Mining with Transaction Fees: A Game on the Block Size",
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"abstract": "In a Bitcoin market, miners participate in blockchain mining with an aim to make profits. Until reaching consensus, PoW-valid blocks (including validated transactions and proper PoW solutions) can be viewed as being successfully mined and are rewarded. There are two types of rewards for miners: fixed block subsidies and time-varying transaction fees. Block subsidies, predetermined by design, are the current major revenue source. Transaction fees, offered by Bitcoin transaction senders to accelerate their transactions, heavily depend on the corresponding transaction size. Thus, a larger-size block tends to contain higher transaction fees and hence more rewards. However, the probability of a miner to successfully mine a block diminishes as the block size increases, since a larger-size block takes a longer time to reach consensus. Thus, the reward included in the block is vitally affected by its size, which is independently decided by a miner. In this paper, we use a game-theoretic approach to study how a miner's payoff, i.e., expected profits, is determined by his block size. More specifically, we derive an expression to characterize the relation between the miner's payoff and block sizes. Besides, we use game theory to analyze how profit-driven miners will manipulate their block sizes to optimize payoff instead of adopting the default block size. We conduct numerical experiments on real-world data collected from Bitcoin to find peaceful equilibrium where miners have no incentive to misbehave. The achieved block sizes thereby give guidelines on the default block size, in order to deter miners from misbehaving. Our analysis suggests a block size of 4 MB.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In a Bitcoin market, miners participate in blockchain mining with an aim to make profits. Until reaching consensus, PoW-valid blocks (including validated transactions and proper PoW solutions) can be viewed as being successfully mined and are rewarded. There are two types of rewards for miners: fixed block subsidies and time-varying transaction fees. Block subsidies, predetermined by design, are the current major revenue source. Transaction fees, offered by Bitcoin transaction senders to accelerate their transactions, heavily depend on the corresponding transaction size. Thus, a larger-size block tends to contain higher transaction fees and hence more rewards. However, the probability of a miner to successfully mine a block diminishes as the block size increases, since a larger-size block takes a longer time to reach consensus. Thus, the reward included in the block is vitally affected by its size, which is independently decided by a miner. In this paper, we use a game-theoretic approach to study how a miner's payoff, i.e., expected profits, is determined by his block size. More specifically, we derive an expression to characterize the relation between the miner's payoff and block sizes. Besides, we use game theory to analyze how profit-driven miners will manipulate their block sizes to optimize payoff instead of adopting the default block size. We conduct numerical experiments on real-world data collected from Bitcoin to find peaceful equilibrium where miners have no incentive to misbehave. The achieved block sizes thereby give guidelines on the default block size, in order to deter miners from misbehaving. Our analysis suggests a block size of 4 MB.",
"__typename": "ArticleAbstractType"
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"normalizedAbstract": "In a Bitcoin market, miners participate in blockchain mining with an aim to make profits. Until reaching consensus, PoW-valid blocks (including validated transactions and proper PoW solutions) can be viewed as being successfully mined and are rewarded. There are two types of rewards for miners: fixed block subsidies and time-varying transaction fees. Block subsidies, predetermined by design, are the current major revenue source. Transaction fees, offered by Bitcoin transaction senders to accelerate their transactions, heavily depend on the corresponding transaction size. Thus, a larger-size block tends to contain higher transaction fees and hence more rewards. However, the probability of a miner to successfully mine a block diminishes as the block size increases, since a larger-size block takes a longer time to reach consensus. Thus, the reward included in the block is vitally affected by its size, which is independently decided by a miner. In this paper, we use a game-theoretic approach to study how a miner's payoff, i.e., expected profits, is determined by his block size. More specifically, we derive an expression to characterize the relation between the miner's payoff and block sizes. Besides, we use game theory to analyze how profit-driven miners will manipulate their block sizes to optimize payoff instead of adopting the default block size. We conduct numerical experiments on real-world data collected from Bitcoin to find peaceful equilibrium where miners have no incentive to misbehave. The achieved block sizes thereby give guidelines on the default block size, in order to deter miners from misbehaving. Our analysis suggests a block size of 4 MB.",
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"authors": [
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"affiliation": "Temple University",
"fullName": "Suhan Jiang",
"givenName": "Suhan",
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{
"affiliation": "Temple University",
"fullName": "Jie Wu",
"givenName": "Jie",
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"proceeding": {
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"title": "2020 6th International Conference on Big Data Computing and Communications (BIGCOM)",
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"title": "Measurement and Analysis of the Bitcoin Networks: A View from Mining Pools",
"normalizedTitle": "Measurement and Analysis of the Bitcoin Networks: A View from Mining Pools",
"abstract": "Bitcoin network has received much attention from both industry and academy due to Bitcoin's recent success. Mining pools, the main components of the Bitcoin network, dominate the computing resources, and play essential roles in network security and performance aspects. Although many existing measurements of the Bitcoin network are available, little is known about the details of mining pool behaviors (e.g., mining revenue and transaction collection strategy) and their effects on the Bitcoin end users (e.g., transaction fees, transaction delay, and transaction acceptance rate). This paper aims to fill this gap with a systematic study of mining pools. We traced over 2.15 hundred thousand blocks from February 2016 to February 2020 and collected over 4.12 TB unconfirmed transactions. Then we conducted a board range of measurements on the pool evolutions, labeled transactions (blocks) as well as real-time network traffics, and discovered new interesting observations and features. Specifically, our measurements showed the following. 1) A few mining pools continuously controlled most of the computing resources of the Bitcoin network. 2) Mining pools were caught in a prisoner's dilemma where mining pools compete to increase their computing resources even though the unit profit of the computing resource decreases. 3) Mining pools were stuck in a Malthusian trap where there is a stage at which the Bitcoin incentives are inadequate for feeding the exponential growth of the computing resources. 4) The market price and transaction fees were not sensitive to the past events of halving block rewards. 5) Feerate played a dominating role in the transaction collection strategy for the top mining pools. Our measurements and analysis helped the Bitcoin community to understand and improve the Bitcoin network.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Bitcoin network has received much attention from both industry and academy due to Bitcoin's recent success. Mining pools, the main components of the Bitcoin network, dominate the computing resources, and play essential roles in network security and performance aspects. Although many existing measurements of the Bitcoin network are available, little is known about the details of mining pool behaviors (e.g., mining revenue and transaction collection strategy) and their effects on the Bitcoin end users (e.g., transaction fees, transaction delay, and transaction acceptance rate). This paper aims to fill this gap with a systematic study of mining pools. We traced over 2.15 hundred thousand blocks from February 2016 to February 2020 and collected over 4.12 TB unconfirmed transactions. Then we conducted a board range of measurements on the pool evolutions, labeled transactions (blocks) as well as real-time network traffics, and discovered new interesting observations and features. Specifically, our measurements showed the following. 1) A few mining pools continuously controlled most of the computing resources of the Bitcoin network. 2) Mining pools were caught in a prisoner's dilemma where mining pools compete to increase their computing resources even though the unit profit of the computing resource decreases. 3) Mining pools were stuck in a Malthusian trap where there is a stage at which the Bitcoin incentives are inadequate for feeding the exponential growth of the computing resources. 4) The market price and transaction fees were not sensitive to the past events of halving block rewards. 5) Feerate played a dominating role in the transaction collection strategy for the top mining pools. Our measurements and analysis helped the Bitcoin community to understand and improve the Bitcoin network.",
"__typename": "ArticleAbstractType"
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"normalizedAbstract": "Bitcoin network has received much attention from both industry and academy due to Bitcoin's recent success. Mining pools, the main components of the Bitcoin network, dominate the computing resources, and play essential roles in network security and performance aspects. Although many existing measurements of the Bitcoin network are available, little is known about the details of mining pool behaviors (e.g., mining revenue and transaction collection strategy) and their effects on the Bitcoin end users (e.g., transaction fees, transaction delay, and transaction acceptance rate). This paper aims to fill this gap with a systematic study of mining pools. We traced over 2.15 hundred thousand blocks from February 2016 to February 2020 and collected over 4.12 TB unconfirmed transactions. Then we conducted a board range of measurements on the pool evolutions, labeled transactions (blocks) as well as real-time network traffics, and discovered new interesting observations and features. Specifically, our measurements showed the following. 1) A few mining pools continuously controlled most of the computing resources of the Bitcoin network. 2) Mining pools were caught in a prisoner's dilemma where mining pools compete to increase their computing resources even though the unit profit of the computing resource decreases. 3) Mining pools were stuck in a Malthusian trap where there is a stage at which the Bitcoin incentives are inadequate for feeding the exponential growth of the computing resources. 4) The market price and transaction fees were not sensitive to the past events of halving block rewards. 5) Feerate played a dominating role in the transaction collection strategy for the top mining pools. Our measurements and analysis helped the Bitcoin community to understand and improve the Bitcoin network.",
"fno": "09160462",
"keywords": [
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"Pricing",
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"Mining Pools",
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"Data Mining",
"Peer To Peer Computing",
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"authors": [
{
"affiliation": "Hong Kong Baptist University,Department of Computer Science",
"fullName": "Canhui Wang",
"givenName": "Canhui",
"surname": "Wang",
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"affiliation": "Hong Kong Baptist University,Department of Computer Science",
"fullName": "Xiaowen Chu",
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{
"affiliation": "Harbin Institute of Technology (Shenzhen),Department of Computer Science",
"fullName": "Yang Qin",
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"abstract": "While previous works have discussed the network delay upper bound that guarantees the consistency of Nakamoto consensus, measuring the actual network latencies and evaluating their impact on miners/pools in Bitcoin remain open questions. This paper fills this gap by: (1) defining metrics that quantify the impact of network latency on the mining network; (2) developing a tool, named miner entanglement (ME), to experimentally evaluate these metrics with a focus on the network latency of the top mining pools; and (3) quantifying the impact of the current network delays on Bitcoin's mining network. For example, we evaluated that Poolin, a Bitcoin mining pool, was able to gain between 0.5% and 1.9% of blocks in addition (i.e., from 36.27 BTC to 137.83 BTC) per week thanks to its low network latency. Moreover, as pools are rational in Bitcoin, we model the strategy a pool would follow to improve its network latency (e.g., by leveraging our ME tool) as a two party game. We show that a Bitcoin mining pool could improve its effective hash rate by up to 4.5%. For a multi-party game, we use a state-of-the-art Bitcoin mining simulator to study the situation where all pools attempt to improve their network latency and show that the largest mining pools would improve their revenue and reach a Nash equilibrium while the smaller mining pools would suffer from a decreased access to the network, and therefore a decreased revenue. These conclusions further incentivize the centralisation of the mining network in Bitcoin, and provide an empirical explanation for the observed tendency of pools to design and rely on low latency private networks.",
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"affiliation": "Delft University of Technology",
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"fullName": "Jiangshan Yu",
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"title": "2017 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)",
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"abstract": "With the availability of the Internet, financial social networks such as StockTwits and SeekingAlpha are emerging to provide opportunities for investors around the world to gather and share their experiences and opinions on the stock market. When these social networks become more and more popular, millions of users join and post huge amount of posts every single day to discuss all kinds of topics related to the market, e.g., the news and events about companies, when to buy and sell shares, which stocks to buy. If a user has a question related his/her investment, he/she needs to browse all the posts returned by searching the stock symbols, however due to the high volume of the posts he/she will drown in the information and hardly to find the answers he/she wants to know. In this paper, we propose to apply a language model with dynamic pseudo relevance feedback to obtain relevant posts in StockTwits, so that people can quickly and easily grasp the ongoing events of their interested financial news. Experiments and a case study on the StockTwits dataset demostrate the effectiveness of the dynamic pseudo relevance feedback.",
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"abstract": "Online Social Networks (OSN s) are an integral part of modern life for sharing thoughts, stories, and news. An ecosystem of influencers generates a flood of content in the form of posts, some of which have an unusually high level of engagement with the influencer's fan base. These posts relate to blossoming topics of discussion that generate particular interest among users: The COVID-19 pandemic is a prominent example. Studying these phenomena provides an understanding of the OSN landscape and requires appropriate methods. This paper presents a methodology to discover notable posts and group them according to their related topic. By combining anomaly detection, graph modelling and community detection techniques, we pinpoint salient events automatically, with the ability to tune the amount of them. We showcase our approach using a large Instagram dataset and extract some notable weekly topics that gained momentum from 1.4 million posts. We then illustrate some use cases ranging from the COVID-19 outbreak to sporting events.",
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"abstract": "Supporting maintenance with 3D object enhanced instruction is one of the key applications of Augmented Reality (AR) in industry. For the breakthrough of AR in maintenance, it is important that the technicians themselves can create AR-instructions and perform the challenging task of placing 3D objects as they know best how to perform a task and what necessary information needs to be displayed. For this challenge, a 3D-content editor is being presented wherein a first step the 3D objects can roughly be placed using a 2D image of the machine, therefore, limiting the time required to access the machine. In a second step, the positions of the 3D objects can be fine-tuned at the machine site using live footage. The key challenges were to develop an easily accessible UI that requires no prior knowledge of AR content creation in a tool that works both with live footage and images and is usable with a touch screen and keyboard/mouse. The 3D-content editor was qualitatively assessed by technicians revealing its general applicability, but also the requirement for a lot of time to gain the necessary experience for positioning 3D objects.",
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"title": "Stroke Extraction of Handwritten Chinese Character Based on Ambiguous Zone Information",
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"abstract": "Stroke extraction plays an important role in the analysis of handwritten Chinese character. Ambiguous zones like intersections and junctions of strokes always bring difficulties for the extraction. The skeleton obtained by thinning algorithm is easy to be distorted in these areas. To solve this problem, an effective method to extract strokes using ambiguous zone information is proposed in this paper. This method uses an 8-neighbour window to detect ambiguous zones on the skeleton first. The removal of ambiguous zones splits the skeleton into several stroke segments. Cosine similarity and smooth degree between local sub-segments are calculated to get the connection coefficient. A reasonable strategy is made to connect stroke segments which satisfy connection coefficient conditions by interpolation. Natural strokes with dynamic information are extracted through the reconstruction and distortions on the skeleton are also modified. The last step detects abrupt turning points to get the final set of strokes. In the experiment, the proposed method obtains obviously better effect when detecting ambiguous zones. The extracted strokes remain good shapes and the position relationship among strokes can be reflected correctly. Results show that the stroke extraction method based on ambiguous zone information achieves high accuracy and fast speed, which can be useful in the related researches on handwritten Chinese character.",
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"content": "Stroke extraction plays an important role in the analysis of handwritten Chinese character. Ambiguous zones like intersections and junctions of strokes always bring difficulties for the extraction. The skeleton obtained by thinning algorithm is easy to be distorted in these areas. To solve this problem, an effective method to extract strokes using ambiguous zone information is proposed in this paper. This method uses an 8-neighbour window to detect ambiguous zones on the skeleton first. The removal of ambiguous zones splits the skeleton into several stroke segments. Cosine similarity and smooth degree between local sub-segments are calculated to get the connection coefficient. A reasonable strategy is made to connect stroke segments which satisfy connection coefficient conditions by interpolation. Natural strokes with dynamic information are extracted through the reconstruction and distortions on the skeleton are also modified. The last step detects abrupt turning points to get the final set of strokes. In the experiment, the proposed method obtains obviously better effect when detecting ambiguous zones. The extracted strokes remain good shapes and the position relationship among strokes can be reflected correctly. Results show that the stroke extraction method based on ambiguous zone information achieves high accuracy and fast speed, which can be useful in the related researches on handwritten Chinese character.",
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"normalizedAbstract": "Stroke extraction plays an important role in the analysis of handwritten Chinese character. Ambiguous zones like intersections and junctions of strokes always bring difficulties for the extraction. The skeleton obtained by thinning algorithm is easy to be distorted in these areas. To solve this problem, an effective method to extract strokes using ambiguous zone information is proposed in this paper. This method uses an 8-neighbour window to detect ambiguous zones on the skeleton first. The removal of ambiguous zones splits the skeleton into several stroke segments. Cosine similarity and smooth degree between local sub-segments are calculated to get the connection coefficient. A reasonable strategy is made to connect stroke segments which satisfy connection coefficient conditions by interpolation. Natural strokes with dynamic information are extracted through the reconstruction and distortions on the skeleton are also modified. The last step detects abrupt turning points to get the final set of strokes. In the experiment, the proposed method obtains obviously better effect when detecting ambiguous zones. The extracted strokes remain good shapes and the position relationship among strokes can be reflected correctly. Results show that the stroke extraction method based on ambiguous zone information achieves high accuracy and fast speed, which can be useful in the related researches on handwritten Chinese character.",
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"Handwritten Character Recognition",
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"fullName": "Enqi Zhan",
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"fullName": "Jianbin Zheng",
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"abstract": "This paper presents a new symbol segmentation method based on AdaBoost with confidence weighted predictions for online handwritten mathematical expressions. The handwritten mathematical expression is preprocessed and rendered to an image. Then for each stroke, we compute three kinds of shape context features (stroke pair, local neighborhood and global shape contexts) with different scales, 21 stroke pair geometric features and symbol classification scores for the current stroke and stroke pair. The stroke pair shape context features covers the current stroke and the following stroke in time series. The local neighborhood shape context features includes the current stroke and its three nearest neighbor strokes in distance while the global shape context features covers the expression. Principal component analysis (PCA) is used for dimensionality reduction. We use AdaBoost with confidence weighted predictions for classification. The method does not use any language model. To our best knowledge, there is no previous work which uses shape context features for symbol segmentation. Experiment results show the new symbol segmentation method achieves good recall and precision on the CROHME 2012 dataset.",
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{
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"content": "This paper presents a new symbol segmentation method based on AdaBoost with confidence weighted predictions for online handwritten mathematical expressions. The handwritten mathematical expression is preprocessed and rendered to an image. Then for each stroke, we compute three kinds of shape context features (stroke pair, local neighborhood and global shape contexts) with different scales, 21 stroke pair geometric features and symbol classification scores for the current stroke and stroke pair. The stroke pair shape context features covers the current stroke and the following stroke in time series. The local neighborhood shape context features includes the current stroke and its three nearest neighbor strokes in distance while the global shape context features covers the expression. Principal component analysis (PCA) is used for dimensionality reduction. We use AdaBoost with confidence weighted predictions for classification. The method does not use any language model. To our best knowledge, there is no previous work which uses shape context features for symbol segmentation. Experiment results show the new symbol segmentation method achieves good recall and precision on the CROHME 2012 dataset.",
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"normalizedAbstract": "This paper presents a new symbol segmentation method based on AdaBoost with confidence weighted predictions for online handwritten mathematical expressions. The handwritten mathematical expression is preprocessed and rendered to an image. Then for each stroke, we compute three kinds of shape context features (stroke pair, local neighborhood and global shape contexts) with different scales, 21 stroke pair geometric features and symbol classification scores for the current stroke and stroke pair. The stroke pair shape context features covers the current stroke and the following stroke in time series. The local neighborhood shape context features includes the current stroke and its three nearest neighbor strokes in distance while the global shape context features covers the expression. Principal component analysis (PCA) is used for dimensionality reduction. We use AdaBoost with confidence weighted predictions for classification. The method does not use any language model. To our best knowledge, there is no previous work which uses shape context features for symbol segmentation. Experiment results show the new symbol segmentation method achieves good recall and precision on the CROHME 2012 dataset.",
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"title": "Multi-Perspective Multi-Modal Trajectory Descriptions for Handwritten Strokes",
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"abstract": "This paper presents a novel approach to generate bag-of-words model based trajectory descriptions for handwritten strokes. We demonstrate how multiple distinct representations can be generated for the same stroke to accommodate writing variations and capture local features at stroke-segment level. The proposed descriptions can be utilized in template matching for handwriting recognition/correction, writer identification, signature verification, etc. The suitability of the proposed shape representations is experimented in a number of settings and used to build a language independent pen-based system for handwriting learning with feedback.",
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"doi": "10.1109/VRW50115.2020.00023",
"title": "The Effects of Multi-sensory Aerial Firefighting Training in Virtual Reality on Situational Awareness, Workload, and Presence",
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"abstract": "Situational Awareness (SA) and managing workload are key factors in aerial firefighting, also known as Air Attack. Training for Air Attack is logistically challenging, risky, expensive, and difficult to practise in naturalistic settings. Simulation training in Virtual Reality (VR) has good potential to mitigate these challenges. This paper explored the application of VR Air Attack training with two multisensory immersive display types, an Oculus Rift CV1 VR Head Mounted Display (HMD) and a 270° Cylindrical Projection Display (CPD). Firefighters training to become Air Support Supervisors were recruited to participate in the study. SA, workload, and sense of presence in the virtual environment were measured. Qualitative data on the firefighters’ user experience and their subjective evaluation of the systems were also collected. This was to gain insights on the suitability of the two systems for Air Attack training, and whether the new systems were an improvement on earlier designs. The results show that the participants experienced a high level of SA, workload and presence in both conditions, with a direct comparison tentatively suggesting that the HMD system invoked slightly higher SA, greater overall presence and lower workload. The collected qualitative data suggest that the participating firefighters appreciated both systems for their realism, extensibility, cost-effectiveness, and accessibility. In particular participants noted that the virtual experience facilitated their understanding of the Air Attack role, and they recommended the use of virtual reality simulation training to supplement existing Air Attack training.",
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"affiliation": "University of Canterbury,HIT Lab NZ",
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"affiliation": "University of Canterbury,HIT Lab NZ",
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"affiliation": "University of Canterbury,School of Product Design HIT Lab NZ",
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"abstract": "Virtual Reality (VR) headsets can open opportunities for users to accomplish complex tasks on large virtual displays, using compact setups. However, interacting with large virtual displays using existing interaction techniques might cause fatigue, especially for precise manipulations, due to the lack of physical surfaces. We designed VXSlate, an interaction technique that uses a large virtual display, as an expansion of a tablet. VXSlate combines a user's head movements, as tracked by the VR headset, and touch interaction on the tablet. The user's head movements position both a virtual representation of the tablet and of the user's hand on the large virtual display. The user's multi-touch interactions perform finely-tuned content manipulations.",
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"abstract": "The purpose of this study is to measure the effectiveness of a MOOC delivered by a leading edu-tech company in India. Effectiveness was evaluated along multiple metric, including the response and learning layers of Kirkpatrick Model. Course statistics and data collected through qualitative and quantitative survey questions and summative assessment performance of the participants were analyzed. The findings indicate that the MOOC was able to address the gaps identified during needs analysis of creating such a MOOC as part of our product training for teachers. Other findings point towards recommendations for designing future MOOCs for our teachers.",
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"affiliation": "Next Education India Pvt Limited",
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"abstract": "Surface registration plays a fundamental role in many applications in computer vision and aims at finding a one-to-one correspondence between surfaces. Conformal mapping based surface registration methods conformally map 2D/3D surfaces onto 2D canonical domains and perform the matching on the 2D plane. This registration framework reduces dimensionality, and the result is intrinsic to Riemannian metric and invariant under isometric deformation. However, conformal mapping will be affected by inconsistent boundaries and non-isometric deformations of surfaces. In this work, we quantify the effects of boundary variation and non-isometric deformation to conformal mappings, and give the theoretical upper bounds for the distortions of conformal mappings under these two factors. Besides giving the thorough theoretical proofs of the theorems, we verified them by concrete experiments using 3D human facial scans with dynamic expressions and varying boundaries. Furthermore, we used the distortion estimates for reducing search range in feature matching of surface registration applications. The experimental results are consistent with the theoretical predictions and also demonstrate the performance improvements in feature tracking.",
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"abstract": "This paper proposes an algorithm for estimation of point projection parameters, based on pruning on the explicit convex hull of the squared distance function. The explicit expression of the squared distance function is deduced. According to the special requirement of point projection, the convex hull of the squared distance function is incrementally constructed. In each step, regions that obviously contain no projection points are eliminated from the base curve or surface, by intersecting the current nearest distance line with the convex hull. When the user-defined tolerances are satisfied, iteration algorithms are used to get the precise projection points. Experimental results show that compared with existing algorithms using clipping circle/sphere or line/plane, this algorithm possesses higher elimination rate and computation speed.",
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"abstract": "Image projection on spheres and their surroundings have been researched for all-around display and motion visualization. However, wide-range projection onto a dynamic sphere can suffer from tracking errors and projection latency. We propose a high-speed projection mapping system for dynamic spheres, and circumferential markers for sphere posture estimation. Marker detection based on ellipse appearance can easily start tracking, and is robust against interactive occlusion; therefore, the system enables projection mapping and rotational visualization for a dynamically moving sphere. We experimentally confirmed sufficient pose estimation accuracy of the circumferential markers compared with initialized dot markers, and sufficient tracking ability against initialization, occlusion, and depth-direction movement. Demonstrations showed accurate, rotation-visualized projection onto a dynamic sphere, which is useful for sports practice applications.",
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"abstract": "An interpolation scheme is constructed to design 2D objects using a Bézier-like rational quadratic trigonometric function with one family of shape parameters. The curve achieved through the constructed scheme, possesses the best possible geometric properties such as convex hull, partition of unity, affine invariance and variation diminishing and some important shape properties such as local and global tension properties which provide good control on the curve and help in modifying the shape as one can desire. Moreover, proposed method performance has smoothness of degree 2.",
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"title": "Evaluation of Ikebana Based on Geometric Shape Arrangement",
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"abstract": "This paper proposes how to evaluate aesthetically pleasing shapes of Ikebana works, that is one of the representative aspects of Japanese traditional culture. In free style category of Ikebana, it is important to include two of three elements: point, line, and face. It is also important to stabilize them by putting mass. Geometric shape Arrangements of Ikebana elements are statistically quantified for Ikebana evaluation.",
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"content": "This paper proposes how to evaluate aesthetically pleasing shapes of Ikebana works, that is one of the representative aspects of Japanese traditional culture. In free style category of Ikebana, it is important to include two of three elements: point, line, and face. It is also important to stabilize them by putting mass. Geometric shape Arrangements of Ikebana elements are statistically quantified for Ikebana evaluation.",
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"normalizedAbstract": "This paper proposes how to evaluate aesthetically pleasing shapes of Ikebana works, that is one of the representative aspects of Japanese traditional culture. In free style category of Ikebana, it is important to include two of three elements: point, line, and face. It is also important to stabilize them by putting mass. Geometric shape Arrangements of Ikebana elements are statistically quantified for Ikebana evaluation.",
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"Urban Areas",
"Face",
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"affiliation": null,
"fullName": "Ayano Miyazaki",
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"fullName": "Hiroko Ota",
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"proceeding": {
"id": "12OmNvRU0lM",
"title": "2008 International Symposium on Information Science and Engieering",
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"groupId": "1002561",
"volume": "1",
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"article": {
"id": "12OmNvJXeB9",
"doi": "10.1109/ISISE.2008.233",
"title": "Computing the Diameters of Abelian Cayley Digraphs with Degree 2",
"normalizedTitle": "Computing the Diameters of Abelian Cayley Digraphs with Degree 2",
"abstract": "Suppose that Z_$ x_{1},y_{1}, x_{2}$_Z and Z_$y_{2}$_Z are four integers.For a given Cayley digraph Z_$\\Omega=Cay(G,\\{a,b\\})$_Z, whereZ_$G=Z^{2}/K, a=(1,0)+K, b=(0,1)+K, $_Z Z_$K= $_Z,Z_$\\alpha=(x_{1},y_{1})$_Z, Z_$\\beta=(x_{2},y_{2})$_Z, an algorithm is given tocompute the four parameters Z_$l, h, m, n$_Z of its Z_$L-$_Zshape tile. Thusthe diameter of this Cayley digraph can be calculated by Z_$d(\\Omega)= \\max\\{l+h-m-2, l+h-n-2\\}$_Z.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Suppose that $ x_{1},y_{1}, x_{2}$ and $y_{2}$ are four integers.For a given Cayley digraph $\\Omega=Cay(G,\\{a,b\\})$, where$G=Z^{2}/K, a=(1,0)+K, b=(0,1)+K, $ $K= $,$\\alpha=(x_{1},y_{1})$, $\\beta=(x_{2},y_{2})$, an algorithm is given tocompute the four parameters $l, h, m, n$ of its $L-$shape tile. Thusthe diameter of this Cayley digraph can be calculated by $d(\\Omega)= \\max\\{l+h-m-2, l+h-n-2\\}$.",
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"normalizedAbstract": "Suppose that - and - are four integers.For a given Cayley digraph -, where- -,-, -, an algorithm is given tocompute the four parameters - of its -shape tile. Thusthe diameter of this Cayley digraph can be calculated by -.",
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"fullName": "Chen Yu",
"givenName": "Chen",
"surname": "Yu",
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{
"affiliation": null,
"fullName": "Chen Baoxing",
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"abstract": "3D scene stylization aims at generating stylized images of the scene from arbitrary novel views following a given set of style examples, while ensuring consistency when rendered from different views. Directly applying methods for image or video stylization to 3D scenes cannot achieve such consistency. Thanks to recently proposed neural radiance fields (NeRF), we are able to represent a 3D scene in a consistent way. Consistent 3D scene stylization can be effectively achieved by stylizing the corresponding NeRF. However, there is a significant domain gap between style examples which are 2D images and NeRF which is an implicit volumetric representation. To address this problem, we propose a novel mutual learning framework for 3D scene stylization that combines a 2D image stylization network and NeRF to fuse the stylization ability of 2D stylization network with the 3D consistency of NeRF. We first pre-train a standard NeRF of the 3D scene to be stylized and replace its color prediction module with a style network to obtain a stylized NeRF. It is followed by distilling the prior knowledge of spatial consistency from NeRF to the 2D stylization network through an introduced consistency loss. We also introduce a mimic loss to supervise the mutual learning of the NeRF style module and fine-tune the 2D stylization decoder. In order to further make our model handle ambiguities of 2D stylization results, we introduce learnable latent codes that obey the probability distributions conditioned on the style. They are attached to training samples as conditional inputs to better learn the style module in our novel stylized NeRF. Experimental results demonstrate that our method is superior to existing approaches in both visual quality and long-range consistency.",
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"abstract": "Gradient pore size porous material is a kind of porous material with asymmetric pore structure, the gradient character could ensure the smaller pore size on the basis of guarantee the larger filtration flux and other homogeneous structure. Therefore, it is of great significance to develop a novel porous material with gradient pore size. In this study, porous TiAl intermetallics support was prepared by solid partial diffusion activation reaction sintering, and then the gradient membrane was coated by additive manufacturing technology. The pore structures were characterized by pore size test. The results revealed that the matrix prepared with elemental powder of 47~74 µm has good match with of 23-25 µm coating material. The matching rule between the maximum pore size of the substrate and the particle size of the membrane was also investigated. This study has practical significance for the preparation of gradient porous materials by additive manufacturing.",
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"content": "Gradient pore size porous material is a kind of porous material with asymmetric pore structure, the gradient character could ensure the smaller pore size on the basis of guarantee the larger filtration flux and other homogeneous structure. Therefore, it is of great significance to develop a novel porous material with gradient pore size. In this study, porous TiAl intermetallics support was prepared by solid partial diffusion activation reaction sintering, and then the gradient membrane was coated by additive manufacturing technology. The pore structures were characterized by pore size test. The results revealed that the matrix prepared with elemental powder of 47~74 µm has good match with of 23-25 µm coating material. The matching rule between the maximum pore size of the substrate and the particle size of the membrane was also investigated. This study has practical significance for the preparation of gradient porous materials by additive manufacturing.",
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"normalizedAbstract": "Gradient pore size porous material is a kind of porous material with asymmetric pore structure, the gradient character could ensure the smaller pore size on the basis of guarantee the larger filtration flux and other homogeneous structure. Therefore, it is of great significance to develop a novel porous material with gradient pore size. In this study, porous TiAl intermetallics support was prepared by solid partial diffusion activation reaction sintering, and then the gradient membrane was coated by additive manufacturing technology. The pore structures were characterized by pore size test. The results revealed that the matrix prepared with elemental powder of 47~74 µm has good match with of 23-25 µm coating material. The matching rule between the maximum pore size of the substrate and the particle size of the membrane was also investigated. This study has practical significance for the preparation of gradient porous materials by additive manufacturing.",
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"keywords": [
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{
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"affiliation": "Wuhan Polytechnic University,School of Mechanical Engineering,Wuhan,Hubei,China",
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{
"affiliation": "Wuhan Polytechnic University,School of Mechanical Engineering,Wuhan,Hubei,China",
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"affiliation": "Wuhan Polytechnic University,School of Mechanical Engineering,Wuhan,Hubei,China",
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