With the rapid development of blockchain technology,the Chinese government has proposed that the commercial use of blockchain services in China should support the national encryption standard,also known as the state s...With the rapid development of blockchain technology,the Chinese government has proposed that the commercial use of blockchain services in China should support the national encryption standard,also known as the state secret algorithm GuoMi algorithm.The original Hyperledger Fabric only supports internationally common encryption algorithms,so it is particularly necessary to enhance support for the national encryption standard.Traditional identity authentication,access control,and security audit technologies have single-point failures,and data can be easily tampered with,leading to trust issues.To address these problems,this paper proposes an optimized and application research plan for Hyperledger Fabric.We study the optimization model of cryptographic components in Hyperledger Fabric,and based on Fabric's pluggable mechanism,we enhance the Fabric architecture with the national encryption standard.In addition,we research key technologies involved in the secure application protocol based on the blockchain.We propose a blockchain-based identity authentication protocol,detailing the design of an identity authentication scheme based on blockchain certificates and Fabric CA,and use a dual-signature method to further improve its security and reliability.Then,we propose a flexible,dynamically configurable real-time access control and security audit mechanism based on blockchain,further enhancing the security of the system.展开更多
The human digital twin(HDT)emerges as a promising human-centric technology in Industry 5.0,but challenges remain in human modeling and simulation.Digital human modeling(DHM)provides solutions for modeling and simulati...The human digital twin(HDT)emerges as a promising human-centric technology in Industry 5.0,but challenges remain in human modeling and simulation.Digital human modeling(DHM)provides solutions for modeling and simulating human physical and cognitive aspects to support ergonomic analysis.However,it has limitations in real-time data usage,personalized services,and timely interaction.The emerging HDT concept offers new possibilities by integrating multi-source data and artificial intelligence for continuous monitoring and assessment.Hence,this paper reviews the evolution from DHM to HDT and proposes a unified HDT framework from a human factors perspective.The framework comprises the physical twin,the virtual twin,and the linkage between these two.The virtual twin integrates human modeling and AI engines to enable model-data-hybrid-enabled simulation.HDT can potentially upgrade traditional ergonomic methods to intelligent services through real-time analysis,timely feedback,and bidirectional interactions.Finally,the future perspectives of HDT for industrial applications as well as technical and social challenges are discussed.In general,this study outlines a human factors perspective on HDT for the first time,which is useful for cross-disciplinary research and human factors innovation to enhance the development of HDT in industry.展开更多
Binaural rendering is of great interest to virtual reality and immersive media. Although humans can naturally use their two ears to perceive the spatial information contained in sounds, it is a challenging task for ma...Binaural rendering is of great interest to virtual reality and immersive media. Although humans can naturally use their two ears to perceive the spatial information contained in sounds, it is a challenging task for machines to achieve binaural rendering since the description of a sound field often requires multiple channels and even the metadata of the sound sources. In addition, the perceived sound varies from person to person even in the same sound field. Previous methods generally rely on individual-dependent head-related transferred function(HRTF)datasets and optimization algorithms that act on HRTFs. In practical applications, there are two major drawbacks to existing methods. The first is a high personalization cost, as traditional methods achieve personalized needs by measuring HRTFs. The second is insufficient accuracy because the optimization goal of traditional methods is to retain another part of information that is more important in perception at the cost of discarding a part of the information. Therefore, it is desirable to develop novel techniques to achieve personalization and accuracy at a low cost. To this end, we focus on the binaural rendering of ambisonic and propose 1) channel-shared encoder and channel-compared attention integrated into neural networks and 2) a loss function quantifying interaural level differences to deal with spatial information. To verify the proposed method, we collect and release the first paired ambisonic-binaural dataset and introduce three metrics to evaluate the content information and spatial information accuracy of the end-to-end methods. Extensive experimental results on the collected dataset demonstrate the superior performance of the proposed method and the shortcomings of previous methods.展开更多
Loan lending plays an important role in our everyday life and powerfully promotes the growth of consumption and the economy. Loan default has been unavoidable, which carries a great risk and may even end up in a finan...Loan lending plays an important role in our everyday life and powerfully promotes the growth of consumption and the economy. Loan default has been unavoidable, which carries a great risk and may even end up in a financial crisis. Therefore, it is particularly important to identify whether a candidate is eligible for receiving a loan. In this paper, we apply Random Forest and XGBoost algorithms to train the prediction model and compare their performance in prediction accuracy. In the feature engineering part, we use the variance threshold method and Variance Inflation Factor method to filter out unimportant features, and then we input those selected features into Random Forest and XGBoost models. It turns out that Random Forest and XGBoost show little difference in the accuracy of their predictions since both get high accuracy of around 0.9 in the loan default cases.展开更多
Background: Weibo is a Twitter-like micro-blog platform in China where people post their real-life events as well as express their feelings in short texts. Since the outbreak of the Covid-19 pandemic, thousands of peo...Background: Weibo is a Twitter-like micro-blog platform in China where people post their real-life events as well as express their feelings in short texts. Since the outbreak of the Covid-19 pandemic, thousands of people have expressed their concerns and worries about the outbreak via Weibo, showing the existence of public panic. Methods: This paper comes up with a sentiment analysis approach to discover public panic. First, we used Octoparse to obtain Weibo posts about the hot topic Covid-19 Pandemic. Second, we break down those sentences into independent words and clean the data by removing stop words. Then, we use the sentiment score function that deals with negative words, adverbs, and sentiment words to get the sentiment score of each Weibo post. Results: We observe the distribution of sentiment scores and get the benchmark to evaluate public panic. Also, we apply the same process to test the mass sentiment under other topics to test the efficiency of the sentiment function, which shows that our function works well.展开更多
Biotechnology, as an emerging technology, has drawn much attention from the public and elicited hot debates in countries around the world and among various stakeholders. Due to the public's limited access to front...Biotechnology, as an emerging technology, has drawn much attention from the public and elicited hot debates in countries around the world and among various stakeholders. Due to the public's limited access to front-line scientific information and scientists, as well as the difficulty of processing complex scientific knowledge, the media have become one of the most important channels for the public to get news about scientific issues such as genetically modified organisms(GMOs). According to framing theory, how the media portray GMO issues may influence audiences' perceptions of those issues. Moreover, different countries and societies have various GMO regulations, policies and public opinion, which also affect the way media cover GMO issues. Thus, it is necessary to investigate how GMO issues are covered in different media outlets across different countries. We conducted a comparative content analysis of media coverage of GMO issues in China, the US and the UK. One mainstream news portal in each of the three countries was chosen(People's Daily for China, The New York Times for the US, and The Guardian for the UK). We collected coverage over eight years, from 2008 to 2015, which yielded 749 pieces of news in total. We examined the sentiments expressed and the generic frames used in coverage of GMO issues. We found that the factual, human interest, conflict and regulation frames were the most common frames used on the three portals, while the sentiments expressed under those frames varied across the media outlets, indicating differences in the state of GMO development, promotion and regulation among the three countries.展开更多
To prevent misuse of privacy,numerous anonymous authentication schemes with linkability and/or traceability have been proposed to ensure different types of accountabilities.Previous schemes cannot simultaneously achie...To prevent misuse of privacy,numerous anonymous authentication schemes with linkability and/or traceability have been proposed to ensure different types of accountabilities.Previous schemes cannot simultaneously achieve public linking and tracing while holding access control,therefore,a new tool named linkable and traceable anonymous authentication with fine-grained access control(LTAA-FGAC)is offered,which is designed to satisfy:(i)access control,i.e.,only authorized users who meet a designated authentication policy are approved to authenticate messages;(ii)public linkability,i.e.,anyone can tell whether two authentications with respect to a common identifier are created by an identical user;(iii)public traceability,i.e.,everyone has the ability to deduce a double-authentication user’s identity from two linked authentications without the help of other parties.We formally define the basic security requirements for the new tool,and also give a generic construction so as to satisfy these requirements.Then,we present a formal security proof and an implementation of our proposed LTAA-FGAC scheme.展开更多
Incremental code editing,as a fundamental task in software development,requires developers to iteratively identify edit locations and modify code.However,existing language model-driven approaches primarily focus on ge...Incremental code editing,as a fundamental task in software development,requires developers to iteratively identify edit locations and modify code.However,existing language model-driven approaches primarily focus on generating edit solutions for a single location,failing to provide comprehensive end-to-end solutions.To address this limitation and support real-world editing scenarios,we propose CoEdPilot,a project-wide interactive code editing recommendation tool.CoEdPilot utilizes edit descriptions and edit history,and recommends the next edit location with solutions across the entire project.It further refines its recommendations based on user editing feedback,enabling an end-to-end,iterative,and interactive editing process.We implement CoEdPilot as a visual studio code extension that monitors user actions,identifies subsequent editing locations,and generates edits throughout the project.Its functionality is powered by a set of back-end language models,which are trained on 180000 high-quality commits from 471 open-source repositories.Extensive experiments demonstrate CoEdPilot's capabilities in accurately identifying editing locations(i.e.,edit location predicted with an accuracy of 85.03%–88.99%)and generating high-quality edit solutions(i.e.,generated edit content with a top-1 exact match rate(EMR)of 33.48%–48.94%).Our case study and user study of 18 participants further validate CoEdPilot's practicability.展开更多
Detecting the phases of the superconducting order parameter is pivotal for unraveling the pairing symmetry of superconducting electrons.Conventional methods for probing these phases have focused on macroscopic interfe...Detecting the phases of the superconducting order parameter is pivotal for unraveling the pairing symmetry of superconducting electrons.Conventional methods for probing these phases have focused on macroscopic interference effects,such as the Josephson effect.However,at the microscopic level,phase interference effects within momentum space have remained elusive due to the inherent difficulty of extracting phase information from individual momentum points.By introducing the hybridization effect between a primary band and its replica bands arising from density wave orders or other interactions,we uncover a novel superconducting phase interference effect at the intersection points on the Fermi surfaces of these bands.This effect elucidates the remarkable anomalies recently observed in the single-particle spectral function through angle-resolved photoemission spectroscopy(ARPES)in(Bi2212)superconductors.It can also emerge in twisted junctions of superconductors with coherent tunneling,offering an alternative framework for probing the relative superconducting phase through twisted superstructures.展开更多
Recent advancements in the field have resulted in significant progress in achieving realistic head reconstruction and manipulation using neural radiance fields(NeRF).Despite these advances,capturing intricate facial d...Recent advancements in the field have resulted in significant progress in achieving realistic head reconstruction and manipulation using neural radiance fields(NeRF).Despite these advances,capturing intricate facial details remains a persistent challenge.Moreover,casually captured input,involving both head poses and camera movements,introduces additional difficulties to existing methods of head avatar reconstruction.To address the challenge posed by video data captured with camera motion,we propose a novel method,AvatarWild,for reconstructing head avatars from monocular videos taken by consumer devices.Notably,our approach decouples the camera pose and head pose,allowing reconstructed avatars to be visualized with different poses and expressions from novel viewpoints.To enhance the visual quality of the reconstructed facial avatar,we introduce a view-dependent detail enhancement module designed to augment local facial details without compromising viewpoint consistency.Our method demonstrates superior performance compared to existing approaches,as evidenced by reconstruction and animation results on both multi-view and single-view datasets.Remarkably,our approach stands out by exclusively relying on video data captured by portable devices,such as smartphones.This not only underscores the practicality of our method but also extends its applicability to real-world scenarios where accessibility and ease of data capture are crucial.展开更多
Dance-driven music generation aims to generate musical pieces conditioned on dance videos.Previous works focus on monophonic or raw audio generation,while the multi-instrument scenario is under-explored.The challenges...Dance-driven music generation aims to generate musical pieces conditioned on dance videos.Previous works focus on monophonic or raw audio generation,while the multi-instrument scenario is under-explored.The challenges associated with dancedriven multi-instrument music(MIDI)generation are twofold:(i)lack of a publicly available multi-instrument MIDI and video paired dataset and(ii)the weak correlation between music and video.To tackle these challenges,we have built the first multi-instrument MIDI and dance paired dataset(D2MIDI).Based on this dataset,we introduce a multi-instrument MIDI generation framework(Dance2MIDI)conditioned on dance video.Firstly,to capture the relationship between dance and music,we employ a graph convolutional network to encode the dance motion.This allows us to extract features related to dance movement and dance style.Secondly,to generate a harmonious rhythm,we utilize a transformer model to decode the drum track sequence,leveraging a cross-attention mechanism.Thirdly,we model the task of generating the remaining tracks based on the drum track as a sequence understanding and completion task.A BERTlike model is employed to comprehend the context of the entire music piece through self-supervised learning.We evaluate the music generated by our framework trained on the D2MIDI dataset and demonstrate that our method achieves state-of-the-art performance.展开更多
Quantum computing has shown great potential in various quantum chemical applications such as drug discovery,material design,and catalyst optimization.Although significant progress has been made in the quantum simulati...Quantum computing has shown great potential in various quantum chemical applications such as drug discovery,material design,and catalyst optimization.Although significant progress has been made in the quantum simulation of simple molecules,ab initio simulation of solid-state materials on quantum computers is still in its early stage,mostly owing to the fact that the system size quickly becomes prohibitively large when approaching the thermodynamic limit.In this work,we introduce an orbital-based multifragment approach on top of the periodic density matrix embedding theory,resulting in a significantly smaller problem size for the current near-term quantum computer.We demonstrate the accuracy and efficiency of our method compared with the conventional methodologies and experiments on solid-state systems with complex electronic structures.These include spin-polarized states of a hydrogen chain(1D-H),the equation of state of a boron nitride layer(h-BN)as well as the magnetic ordering in nickel oxide(NiO),a prototypical strongly correlated solid.Our results suggest that quantum embedding combined with a chemically intuitive fragmentation can greatly advance quantum simulation of realistic materials,thereby paving the way for solving important yet classically hard industrial problems on near-term quantum devices.展开更多
Self-supervised learning aims to learn a universal feature representation without labels.To date,most existing self-supervised learning methods are designed and optimized for image classification.These pre-trained mod...Self-supervised learning aims to learn a universal feature representation without labels.To date,most existing self-supervised learning methods are designed and optimized for image classification.These pre-trained models can be sub-optimal for dense prediction tasks due to the discrepancy between image-level prediction and pixel-level prediction.To fill this gap,we aim to design an effective,dense self-supervised learning framework that directly works at the level of pixels(or local features)by taking into account the correspondence between local features.Specifically,we present dense contrastive learning(DenseCL),which implements self-supervised learning by optimizing a pairwise contrastive(dis)similarity loss at the pixel level between two views of input images.Compared to the supervised ImageNet pre-training and other self-supervised learning methods,our self-supervised DenseCL pretraining demonstrates consistently superior performance when transferring to downstream dense prediction tasks including object detection,semantic segmentation and instance segmentation.Specifically,our approach significantly outperforms the strong MoCo-v2 by 2.0%AP on PASCAL VOC object detection,1.1%AP on COCO object detection,0.9%AP on COCO instance segmentation,3.0%mIoU on PASCAL VOC semantic segmentation and 1.8%mIoU on Cityscapes semantic segmentation.The improvements are up to 3.5%AP and 8.8%mIoU over MoCo-v2,and 6.1%AP and 6.1%mIoU over supervised counterpart with frozen-backbone evaluation protocol.展开更多
Containerized microservices have been widely deployed in the industry.Meanwhile,security issues also arise.Many security enhancement mechanisms for containerized microservices require predefined rules and policies.How...Containerized microservices have been widely deployed in the industry.Meanwhile,security issues also arise.Many security enhancement mechanisms for containerized microservices require predefined rules and policies.However,it is challenging when it comes to thousands of microservices and a massive amount of real-time unstructured data.Hence,automatic policy generation becomes indispensable.In this paper,we focus on the automatic solution for the security problem:irregular traffic detection for RPCs.We propose Informer,a two-phase machine learning framework to track the traffic of each RPC and automatically report anomalous points.We first identify RPC chain patterns using density-based clustering techniques and build a graph for each critical pattern.Next,we solve the irregular RPC traffic detection problem as a prediction problem for attributed graphs with time series by leveraging spatial-temporal graph convolution networks.Since the framework builds multiple models and makes individual predictions for each RPC chain pattern,it can be efficiently updated upon legitimate changes in any graphs.In evaluations,we applied Informer to a dataset containing more than 7 billion lines of raw RPC logs sampled from a large Kubernetes system for two weeks.We provide two case studies of detected real-world threats.As a result,our framework found fine-grained RPC chain patterns and accurately captured the anomalies in a dynamic and complicated microservice production scenario,which demonstrates the effectiveness of Informer.Furthermore,we extensively evaluated the risk of adversarial attacks for our prediction model under different reality constraints and showed that the model is robust to such attacks in most real-world scenarios.展开更多
The second-order random walk has recently been shown to effectively improve the accuracy in graph analysis tasks.Existing work mainly focuses on centralized second-order random walk(SOW)algorithms.SOW algorithms rely ...The second-order random walk has recently been shown to effectively improve the accuracy in graph analysis tasks.Existing work mainly focuses on centralized second-order random walk(SOW)algorithms.SOW algorithms rely on edge-to-edge transition probabilities to generate next random steps.However,it is prohibitively costly to store all the probabilities for large-scale graphs,and restricting the number of probabilities to consider can negatively impact the accuracy of graph analysis tasks.In this paper,we propose and study an alternative approach,SOOP(second-order random walks with on-demand probability computation),that avoids the space overhead by computing the edge-to-edge transition probabilities on demand during the random walk.However,the same probabilities may be computed multiple times when the same edge appears multiple times in SOW,incurring extra cost for redundant computation and communication.We propose two optimization techniques that reduce the complexity of computing edge-to-edge transition probabilities to generate next random steps,and reduce the cost of communicating out-neighbors for the probability computation,respectively.Our experiments on real-world and synthetic graphs show that SOOP achieves orders of magnitude better performance than baseline precompute solutions,and it can efficiently computes SOW algorithms on billion-scale graphs.展开更多
Energy harvesting technologies allow wireless devices to be recharged by the surrounding environment, providing wireless sensor networks (WSNs) with higher performance and longer lifetime. However, directly building a...Energy harvesting technologies allow wireless devices to be recharged by the surrounding environment, providing wireless sensor networks (WSNs) with higher performance and longer lifetime. However, directly building a wireless sensor network with energy harvesting nodes is very costly. A compromise is upgrading existing networks with energy harvesting technologies. In this paper, we focus on prolonging the lifetime of WSNs with the help of energy harvesting relays (EHRs). EHRs are responsible for forwarding data for sensor nodes, allowing them to become terminals and thus extending their lifetime. We aim to deploy a minimum number of relays covering the whole network. As EHRs have several special properties such as the energy harvesting and depletion rate, it brings great research challenges to seek an optimal deployment strategy. To this end, we propose an approximation algorithm named Effective Relay Deployment Algorithm, which can be divided into two phases: disk covering and connector insertion using the partitioning technique and the Steinerization technique, respectively. Based on probabilistic analysis, we further optimize the performance ratio of our algorithm to (5 + 6/K) where K is an integer denoting the side length of a cell after partitioning. Our extensive simulation results show that our algorithm can reduce the number of EHRs to be deployed by up to 45% compared with previous work and thus validate the efficiency and effectiveness of our solution.展开更多
China has made remarkable progress in the socioeconomic sphere since the reform and opening up in 1978;further,its success in large-scale poverty alleviation has been warmly applauded by the international community.Af...China has made remarkable progress in the socioeconomic sphere since the reform and opening up in 1978;further,its success in large-scale poverty alleviation has been warmly applauded by the international community.After the 18th National Congress of the Communist Party of China(CPC)in 2012,the CPC Central Committee and the State Council decided to adopt targeted poverty reduction and alleviation as the basic strategy for providing development-oriented poverty alleviation.The report delivered at the 19th National Congress of the CPC in 2017 proposed that“we must ensure that by the year 2020,all rural residents living below the current poverty line have been lifted out of poverty,and poverty is eliminated in all poor countries and regions.”展开更多
Generating emotional talking faces from a single portrait image remains a significant challenge. The simultaneous achievement of expressive emotional talking and accurate lip-sync is particularly difficult, as express...Generating emotional talking faces from a single portrait image remains a significant challenge. The simultaneous achievement of expressive emotional talking and accurate lip-sync is particularly difficult, as expressiveness is often compromised for lip-sync accuracy. Prevailing generative works usually struggle to juggle to generate subtle variations of emotional expression and lip-synchronized talking. To address these challenges, we suggest modeling the implicit and explicit correlations between audio and emotional talking faces with a unified framework. As human emotional expressions usually present subtle and implicit relations with speech audio, we propose incorporating audio and emotional style embeddings into the diffusion-based generation process, for realistic generation while concentrating on emotional expressions. We then propose lip-based explicit correlation learning to construct a strong mapping of audio to lip motions, assuring lip-audio synchronization. Furthermore, we deploy a video-to-video rendering module to transfer expressions and lip motions from a proxy 3D avatar to an arbitrary portrait. Both quantitatively and qualitatively, MagicTalk outperforms state-of-the-art methods in terms of expressiveness, lip-sync, and perceptual quality.展开更多
In this paper,we present TexPro,a novel method for high-fidelity material generation for input 3D meshes given text prompts.Unlike existing text-conditioned texture generation methods that typically generate RGB textu...In this paper,we present TexPro,a novel method for high-fidelity material generation for input 3D meshes given text prompts.Unlike existing text-conditioned texture generation methods that typically generate RGB textures with baked lighting,TexPro is able to produce diverse texture maps via procedural material modeling,which enables physically-based rendering,relighting,and additional benefits inherent to procedural materials.Specifically,we first generate multi-view reference images given the input textual prompt by employing the latest text-to-image model.We then derive texture maps through rendering-based optimization with recent differentiable procedural materials.To this end,we design several techniques to handle the misalignment between the generated multi-view images and 3D meshes,and introduce a novel material agent that enhances material classification and matching by exploring both part-level understanding and object-aware material reasoning.Experiments demonstrate the superiority of the proposed method over existing SOTAs,and its capability of relighting.展开更多
基金supported by Fujian Provincial Social Science Foundation Public Security Theory Research Project(FJ2023TWGA004)Education and Scientific Research Special Project of Fujian Provincial Department of Finance(Research on the Application of Blockchain Technology in Prison Law Enforcement Management),under National Key R&D Program of China(2020YFB1005500)。
文摘With the rapid development of blockchain technology,the Chinese government has proposed that the commercial use of blockchain services in China should support the national encryption standard,also known as the state secret algorithm GuoMi algorithm.The original Hyperledger Fabric only supports internationally common encryption algorithms,so it is particularly necessary to enhance support for the national encryption standard.Traditional identity authentication,access control,and security audit technologies have single-point failures,and data can be easily tampered with,leading to trust issues.To address these problems,this paper proposes an optimized and application research plan for Hyperledger Fabric.We study the optimization model of cryptographic components in Hyperledger Fabric,and based on Fabric's pluggable mechanism,we enhance the Fabric architecture with the national encryption standard.In addition,we research key technologies involved in the secure application protocol based on the blockchain.We propose a blockchain-based identity authentication protocol,detailing the design of an identity authentication scheme based on blockchain certificates and Fabric CA,and use a dual-signature method to further improve its security and reliability.Then,we propose a flexible,dynamically configurable real-time access control and security audit mechanism based on blockchain,further enhancing the security of the system.
基金Supported by National Natural Science Foundation of China(Grant No.72071179)ZJU-Sunon Joint Research Center of Smart Furniture,Zhejiang University,China.
文摘The human digital twin(HDT)emerges as a promising human-centric technology in Industry 5.0,but challenges remain in human modeling and simulation.Digital human modeling(DHM)provides solutions for modeling and simulating human physical and cognitive aspects to support ergonomic analysis.However,it has limitations in real-time data usage,personalized services,and timely interaction.The emerging HDT concept offers new possibilities by integrating multi-source data and artificial intelligence for continuous monitoring and assessment.Hence,this paper reviews the evolution from DHM to HDT and proposes a unified HDT framework from a human factors perspective.The framework comprises the physical twin,the virtual twin,and the linkage between these two.The virtual twin integrates human modeling and AI engines to enable model-data-hybrid-enabled simulation.HDT can potentially upgrade traditional ergonomic methods to intelligent services through real-time analysis,timely feedback,and bidirectional interactions.Finally,the future perspectives of HDT for industrial applications as well as technical and social challenges are discussed.In general,this study outlines a human factors perspective on HDT for the first time,which is useful for cross-disciplinary research and human factors innovation to enhance the development of HDT in industry.
基金supported in part by the National Natural Science Foundation of China (62176059, 62101136)。
文摘Binaural rendering is of great interest to virtual reality and immersive media. Although humans can naturally use their two ears to perceive the spatial information contained in sounds, it is a challenging task for machines to achieve binaural rendering since the description of a sound field often requires multiple channels and even the metadata of the sound sources. In addition, the perceived sound varies from person to person even in the same sound field. Previous methods generally rely on individual-dependent head-related transferred function(HRTF)datasets and optimization algorithms that act on HRTFs. In practical applications, there are two major drawbacks to existing methods. The first is a high personalization cost, as traditional methods achieve personalized needs by measuring HRTFs. The second is insufficient accuracy because the optimization goal of traditional methods is to retain another part of information that is more important in perception at the cost of discarding a part of the information. Therefore, it is desirable to develop novel techniques to achieve personalization and accuracy at a low cost. To this end, we focus on the binaural rendering of ambisonic and propose 1) channel-shared encoder and channel-compared attention integrated into neural networks and 2) a loss function quantifying interaural level differences to deal with spatial information. To verify the proposed method, we collect and release the first paired ambisonic-binaural dataset and introduce three metrics to evaluate the content information and spatial information accuracy of the end-to-end methods. Extensive experimental results on the collected dataset demonstrate the superior performance of the proposed method and the shortcomings of previous methods.
文摘Loan lending plays an important role in our everyday life and powerfully promotes the growth of consumption and the economy. Loan default has been unavoidable, which carries a great risk and may even end up in a financial crisis. Therefore, it is particularly important to identify whether a candidate is eligible for receiving a loan. In this paper, we apply Random Forest and XGBoost algorithms to train the prediction model and compare their performance in prediction accuracy. In the feature engineering part, we use the variance threshold method and Variance Inflation Factor method to filter out unimportant features, and then we input those selected features into Random Forest and XGBoost models. It turns out that Random Forest and XGBoost show little difference in the accuracy of their predictions since both get high accuracy of around 0.9 in the loan default cases.
文摘Background: Weibo is a Twitter-like micro-blog platform in China where people post their real-life events as well as express their feelings in short texts. Since the outbreak of the Covid-19 pandemic, thousands of people have expressed their concerns and worries about the outbreak via Weibo, showing the existence of public panic. Methods: This paper comes up with a sentiment analysis approach to discover public panic. First, we used Octoparse to obtain Weibo posts about the hot topic Covid-19 Pandemic. Second, we break down those sentences into independent words and clean the data by removing stop words. Then, we use the sentiment score function that deals with negative words, adverbs, and sentiment words to get the sentiment score of each Weibo post. Results: We observe the distribution of sentiment scores and get the benchmark to evaluate public panic. Also, we apply the same process to test the mass sentiment under other topics to test the efficiency of the sentiment function, which shows that our function works well.
基金supported by the Science Popularization and Risk Communication of Transgenic Biotechnologies project (grant ID:2016ZX08015002)
文摘Biotechnology, as an emerging technology, has drawn much attention from the public and elicited hot debates in countries around the world and among various stakeholders. Due to the public's limited access to front-line scientific information and scientists, as well as the difficulty of processing complex scientific knowledge, the media have become one of the most important channels for the public to get news about scientific issues such as genetically modified organisms(GMOs). According to framing theory, how the media portray GMO issues may influence audiences' perceptions of those issues. Moreover, different countries and societies have various GMO regulations, policies and public opinion, which also affect the way media cover GMO issues. Thus, it is necessary to investigate how GMO issues are covered in different media outlets across different countries. We conducted a comparative content analysis of media coverage of GMO issues in China, the US and the UK. One mainstream news portal in each of the three countries was chosen(People's Daily for China, The New York Times for the US, and The Guardian for the UK). We collected coverage over eight years, from 2008 to 2015, which yielded 749 pieces of news in total. We examined the sentiments expressed and the generic frames used in coverage of GMO issues. We found that the factual, human interest, conflict and regulation frames were the most common frames used on the three portals, while the sentiments expressed under those frames varied across the media outlets, indicating differences in the state of GMO development, promotion and regulation among the three countries.
基金supported by the National Natural Science Foundation of China(Grant Nos.U2001205,61932010)Guangdong Basic and Applied Basic Research Foundation(Nos.2023B1515040020,2019B030302008)Guangdong Provincial Key Laboratory of Power System Network Security(No.GPKLPSNS-2022-KF-05).
文摘To prevent misuse of privacy,numerous anonymous authentication schemes with linkability and/or traceability have been proposed to ensure different types of accountabilities.Previous schemes cannot simultaneously achieve public linking and tracing while holding access control,therefore,a new tool named linkable and traceable anonymous authentication with fine-grained access control(LTAA-FGAC)is offered,which is designed to satisfy:(i)access control,i.e.,only authorized users who meet a designated authentication policy are approved to authenticate messages;(ii)public linkability,i.e.,anyone can tell whether two authentications with respect to a common identifier are created by an identical user;(iii)public traceability,i.e.,everyone has the ability to deduce a double-authentication user’s identity from two linked authentications without the help of other parties.We formally define the basic security requirements for the new tool,and also give a generic construction so as to satisfy these requirements.Then,we present a formal security proof and an implementation of our proposed LTAA-FGAC scheme.
基金supported by the National Key Technology Research and Development Program of China under Grant No.2023YFB4503802the Minister of Education of Singapore under Grant Nos.MOE-T2EP20124-0017 and MOET32020-0004+1 种基金the National Research Foundation of Singapore and the Cyber Security Agency under its National Cybersecurity Research and Development Programme under Grant No.NCRP25-P04-TAICeNDefence Science Organisation National Laboratories under the AI Singapore Programme of AISG Award No.AISG2-GC-2023-008。
文摘Incremental code editing,as a fundamental task in software development,requires developers to iteratively identify edit locations and modify code.However,existing language model-driven approaches primarily focus on generating edit solutions for a single location,failing to provide comprehensive end-to-end solutions.To address this limitation and support real-world editing scenarios,we propose CoEdPilot,a project-wide interactive code editing recommendation tool.CoEdPilot utilizes edit descriptions and edit history,and recommends the next edit location with solutions across the entire project.It further refines its recommendations based on user editing feedback,enabling an end-to-end,iterative,and interactive editing process.We implement CoEdPilot as a visual studio code extension that monitors user actions,identifies subsequent editing locations,and generates edits throughout the project.Its functionality is powered by a set of back-end language models,which are trained on 180000 high-quality commits from 471 open-source repositories.Extensive experiments demonstrate CoEdPilot's capabilities in accurately identifying editing locations(i.e.,edit location predicted with an accuracy of 85.03%–88.99%)and generating high-quality edit solutions(i.e.,generated edit content with a top-1 exact match rate(EMR)of 33.48%–48.94%).Our case study and user study of 18 participants further validate CoEdPilot's practicability.
基金supported by the National Natural Science Foundation of China(12488201)the National Key Research and Development Program of China(2021YFA1401800).
文摘Detecting the phases of the superconducting order parameter is pivotal for unraveling the pairing symmetry of superconducting electrons.Conventional methods for probing these phases have focused on macroscopic interference effects,such as the Josephson effect.However,at the microscopic level,phase interference effects within momentum space have remained elusive due to the inherent difficulty of extracting phase information from individual momentum points.By introducing the hybridization effect between a primary band and its replica bands arising from density wave orders or other interactions,we uncover a novel superconducting phase interference effect at the intersection points on the Fermi surfaces of these bands.This effect elucidates the remarkable anomalies recently observed in the single-particle spectral function through angle-resolved photoemission spectroscopy(ARPES)in(Bi2212)superconductors.It can also emerge in twisted junctions of superconductors with coherent tunneling,offering an alternative framework for probing the relative superconducting phase through twisted superstructures.
基金supported by National Natural Science Foundation of China(No.6247075018 and No.62322210)the Innovation Funding of ICT,CAS(No.E461020)+1 种基金Beijing Munici-pal Natural Science Foundation for Distinguished Young Scholars(No.JQ21013)Beijing Municipal Science and Technology Commission(No.Z231100005923031).
文摘Recent advancements in the field have resulted in significant progress in achieving realistic head reconstruction and manipulation using neural radiance fields(NeRF).Despite these advances,capturing intricate facial details remains a persistent challenge.Moreover,casually captured input,involving both head poses and camera movements,introduces additional difficulties to existing methods of head avatar reconstruction.To address the challenge posed by video data captured with camera motion,we propose a novel method,AvatarWild,for reconstructing head avatars from monocular videos taken by consumer devices.Notably,our approach decouples the camera pose and head pose,allowing reconstructed avatars to be visualized with different poses and expressions from novel viewpoints.To enhance the visual quality of the reconstructed facial avatar,we introduce a view-dependent detail enhancement module designed to augment local facial details without compromising viewpoint consistency.Our method demonstrates superior performance compared to existing approaches,as evidenced by reconstruction and animation results on both multi-view and single-view datasets.Remarkably,our approach stands out by exclusively relying on video data captured by portable devices,such as smartphones.This not only underscores the practicality of our method but also extends its applicability to real-world scenarios where accessibility and ease of data capture are crucial.
基金supported by the National Social Science Foundation Art Project(No.20BC040)China Scholarship Council(CSC)Grant(No.202306320525).
文摘Dance-driven music generation aims to generate musical pieces conditioned on dance videos.Previous works focus on monophonic or raw audio generation,while the multi-instrument scenario is under-explored.The challenges associated with dancedriven multi-instrument music(MIDI)generation are twofold:(i)lack of a publicly available multi-instrument MIDI and video paired dataset and(ii)the weak correlation between music and video.To tackle these challenges,we have built the first multi-instrument MIDI and dance paired dataset(D2MIDI).Based on this dataset,we introduce a multi-instrument MIDI generation framework(Dance2MIDI)conditioned on dance video.Firstly,to capture the relationship between dance and music,we employ a graph convolutional network to encode the dance motion.This allows us to extract features related to dance movement and dance style.Secondly,to generate a harmonious rhythm,we utilize a transformer model to decode the drum track sequence,leveraging a cross-attention mechanism.Thirdly,we model the task of generating the remaining tracks based on the drum track as a sequence understanding and completion task.A BERTlike model is employed to comprehend the context of the entire music piece through self-supervised learning.We evaluate the music generated by our framework trained on the D2MIDI dataset and demonstrate that our method achieves state-of-the-art performance.
文摘Quantum computing has shown great potential in various quantum chemical applications such as drug discovery,material design,and catalyst optimization.Although significant progress has been made in the quantum simulation of simple molecules,ab initio simulation of solid-state materials on quantum computers is still in its early stage,mostly owing to the fact that the system size quickly becomes prohibitively large when approaching the thermodynamic limit.In this work,we introduce an orbital-based multifragment approach on top of the periodic density matrix embedding theory,resulting in a significantly smaller problem size for the current near-term quantum computer.We demonstrate the accuracy and efficiency of our method compared with the conventional methodologies and experiments on solid-state systems with complex electronic structures.These include spin-polarized states of a hydrogen chain(1D-H),the equation of state of a boron nitride layer(h-BN)as well as the magnetic ordering in nickel oxide(NiO),a prototypical strongly correlated solid.Our results suggest that quantum embedding combined with a chemically intuitive fragmentation can greatly advance quantum simulation of realistic materials,thereby paving the way for solving important yet classically hard industrial problems on near-term quantum devices.
文摘Self-supervised learning aims to learn a universal feature representation without labels.To date,most existing self-supervised learning methods are designed and optimized for image classification.These pre-trained models can be sub-optimal for dense prediction tasks due to the discrepancy between image-level prediction and pixel-level prediction.To fill this gap,we aim to design an effective,dense self-supervised learning framework that directly works at the level of pixels(or local features)by taking into account the correspondence between local features.Specifically,we present dense contrastive learning(DenseCL),which implements self-supervised learning by optimizing a pairwise contrastive(dis)similarity loss at the pixel level between two views of input images.Compared to the supervised ImageNet pre-training and other self-supervised learning methods,our self-supervised DenseCL pretraining demonstrates consistently superior performance when transferring to downstream dense prediction tasks including object detection,semantic segmentation and instance segmentation.Specifically,our approach significantly outperforms the strong MoCo-v2 by 2.0%AP on PASCAL VOC object detection,1.1%AP on COCO object detection,0.9%AP on COCO instance segmentation,3.0%mIoU on PASCAL VOC semantic segmentation and 1.8%mIoU on Cityscapes semantic segmentation.The improvements are up to 3.5%AP and 8.8%mIoU over MoCo-v2,and 6.1%AP and 6.1%mIoU over supervised counterpart with frozen-backbone evaluation protocol.
基金supported by the National Science Foundation of the United States under Grant Nos.1801751 and 1956364.
文摘Containerized microservices have been widely deployed in the industry.Meanwhile,security issues also arise.Many security enhancement mechanisms for containerized microservices require predefined rules and policies.However,it is challenging when it comes to thousands of microservices and a massive amount of real-time unstructured data.Hence,automatic policy generation becomes indispensable.In this paper,we focus on the automatic solution for the security problem:irregular traffic detection for RPCs.We propose Informer,a two-phase machine learning framework to track the traffic of each RPC and automatically report anomalous points.We first identify RPC chain patterns using density-based clustering techniques and build a graph for each critical pattern.Next,we solve the irregular RPC traffic detection problem as a prediction problem for attributed graphs with time series by leveraging spatial-temporal graph convolution networks.Since the framework builds multiple models and makes individual predictions for each RPC chain pattern,it can be efficiently updated upon legitimate changes in any graphs.In evaluations,we applied Informer to a dataset containing more than 7 billion lines of raw RPC logs sampled from a large Kubernetes system for two weeks.We provide two case studies of detected real-world threats.As a result,our framework found fine-grained RPC chain patterns and accurately captured the anomalies in a dynamic and complicated microservice production scenario,which demonstrates the effectiveness of Informer.Furthermore,we extensively evaluated the risk of adversarial attacks for our prediction model under different reality constraints and showed that the model is robust to such attacks in most real-world scenarios.
文摘The second-order random walk has recently been shown to effectively improve the accuracy in graph analysis tasks.Existing work mainly focuses on centralized second-order random walk(SOW)algorithms.SOW algorithms rely on edge-to-edge transition probabilities to generate next random steps.However,it is prohibitively costly to store all the probabilities for large-scale graphs,and restricting the number of probabilities to consider can negatively impact the accuracy of graph analysis tasks.In this paper,we propose and study an alternative approach,SOOP(second-order random walks with on-demand probability computation),that avoids the space overhead by computing the edge-to-edge transition probabilities on demand during the random walk.However,the same probabilities may be computed multiple times when the same edge appears multiple times in SOW,incurring extra cost for redundant computation and communication.We propose two optimization techniques that reduce the complexity of computing edge-to-edge transition probabilities to generate next random steps,and reduce the cost of communicating out-neighbors for the probability computation,respectively.Our experiments on real-world and synthetic graphs show that SOOP achieves orders of magnitude better performance than baseline precompute solutions,and it can efficiently computes SOW algorithms on billion-scale graphs.
基金This work was supported by the Key-Area Research and Development Program of Guangdong Province of China under Grant No.2020B0101390001the Shanghai Municipal Science and Technology Major Project of China under Grant No.2021SHZDZX0102+1 种基金the National Natural Science Foundation of China under Grant No.62072228the Fundamental Research Funds for the Central Universities of China,the Collaborative Innovation Center of Novel Software Technology and Industrialization of Jiangsu Province of China,and the Jiangsu Innovation and Entrepreneurship(Shuangchuang)Program of China.
文摘Energy harvesting technologies allow wireless devices to be recharged by the surrounding environment, providing wireless sensor networks (WSNs) with higher performance and longer lifetime. However, directly building a wireless sensor network with energy harvesting nodes is very costly. A compromise is upgrading existing networks with energy harvesting technologies. In this paper, we focus on prolonging the lifetime of WSNs with the help of energy harvesting relays (EHRs). EHRs are responsible for forwarding data for sensor nodes, allowing them to become terminals and thus extending their lifetime. We aim to deploy a minimum number of relays covering the whole network. As EHRs have several special properties such as the energy harvesting and depletion rate, it brings great research challenges to seek an optimal deployment strategy. To this end, we propose an approximation algorithm named Effective Relay Deployment Algorithm, which can be divided into two phases: disk covering and connector insertion using the partitioning technique and the Steinerization technique, respectively. Based on probabilistic analysis, we further optimize the performance ratio of our algorithm to (5 + 6/K) where K is an integer denoting the side length of a cell after partitioning. Our extensive simulation results show that our algorithm can reduce the number of EHRs to be deployed by up to 45% compared with previous work and thus validate the efficiency and effectiveness of our solution.
文摘China has made remarkable progress in the socioeconomic sphere since the reform and opening up in 1978;further,its success in large-scale poverty alleviation has been warmly applauded by the international community.After the 18th National Congress of the Communist Party of China(CPC)in 2012,the CPC Central Committee and the State Council decided to adopt targeted poverty reduction and alleviation as the basic strategy for providing development-oriented poverty alleviation.The report delivered at the 19th National Congress of the CPC in 2017 proposed that“we must ensure that by the year 2020,all rural residents living below the current poverty line have been lifted out of poverty,and poverty is eliminated in all poor countries and regions.”
文摘Generating emotional talking faces from a single portrait image remains a significant challenge. The simultaneous achievement of expressive emotional talking and accurate lip-sync is particularly difficult, as expressiveness is often compromised for lip-sync accuracy. Prevailing generative works usually struggle to juggle to generate subtle variations of emotional expression and lip-synchronized talking. To address these challenges, we suggest modeling the implicit and explicit correlations between audio and emotional talking faces with a unified framework. As human emotional expressions usually present subtle and implicit relations with speech audio, we propose incorporating audio and emotional style embeddings into the diffusion-based generation process, for realistic generation while concentrating on emotional expressions. We then propose lip-based explicit correlation learning to construct a strong mapping of audio to lip motions, assuring lip-audio synchronization. Furthermore, we deploy a video-to-video rendering module to transfer expressions and lip motions from a proxy 3D avatar to an arbitrary portrait. Both quantitatively and qualitatively, MagicTalk outperforms state-of-the-art methods in terms of expressiveness, lip-sync, and perceptual quality.
基金supported by the National Natural Science Foundation of China(No.62441222)the Information Technology Center and State Key Lab of CAD&CG,Zhejiang University。
文摘In this paper,we present TexPro,a novel method for high-fidelity material generation for input 3D meshes given text prompts.Unlike existing text-conditioned texture generation methods that typically generate RGB textures with baked lighting,TexPro is able to produce diverse texture maps via procedural material modeling,which enables physically-based rendering,relighting,and additional benefits inherent to procedural materials.Specifically,we first generate multi-view reference images given the input textual prompt by employing the latest text-to-image model.We then derive texture maps through rendering-based optimization with recent differentiable procedural materials.To this end,we design several techniques to handle the misalignment between the generated multi-view images and 3D meshes,and introduce a novel material agent that enhances material classification and matching by exploring both part-level understanding and object-aware material reasoning.Experiments demonstrate the superiority of the proposed method over existing SOTAs,and its capability of relighting.
