Attention-Enhanced Voice Portrait Model Using Generative Adversarial Network

Voice portrait technology has explored and established the relationship between speakers’ voices and their facialfeatures, aiming to generate corresponding facial characteristics by providing the voice of an unknown speaker.Due to its powerful advantages in image generation, Generative Adversarial Networks (GANs) have now beenwidely applied across various fields. The existing Voice2Face methods for voice portraits are primarily based onGANs trained on voice-face paired datasets. However, voice portrait models solely constructed on GANs facelimitations in image generation quality and struggle to maintain facial similarity. Additionally, the training processis relatively unstable, thereby affecting the overall generative performance of the model. To overcome the abovechallenges,wepropose a novel deepGenerativeAdversarialNetworkmodel for audio-visual synthesis, namedAVPGAN(Attention-enhanced Voice Portrait Model using Generative Adversarial Network). This model is based ona convolutional attention mechanism and is capable of generating corresponding facial images from the voice ofan unknown speaker. Firstly, to address the issue of training instability, we integrate convolutional neural networkswith deep GANs. In the network architecture, we apply spectral normalization to constrain the variation of thediscriminator, preventing issues such as mode collapse. Secondly, to enhance the model’s ability to extract relevantfeatures between the two modalities, we propose a voice portrait model based on convolutional attention. Thismodel learns the mapping relationship between voice and facial features in a common space from both channeland spatial dimensions independently. Thirdly, to enhance the quality of generated faces, we have incorporated adegradation removal module and utilized pretrained facial GANs as facial priors to repair and enhance the clarityof the generated facial images. Experimental results demonstrate that our AVP-GAN achieved a cosine similarity of0.511, outperforming the performance of our comparison model, and effectively achieved the generation of highqualityfacial images corresponding to a speaker’s voice.

Intention and Standing Image:Interpretation of the Aesthetic Intention of Arhat Portrait on Mount Wutai

Mount Wutai,a place endowed with fine spirits of humanities and prolific remains of Arhats portrait art.The existing Mount Wutai Arhats portrait spans the Ming,Qing,and Republic of China periods,and through the artistic creation of successive generations of literati painters,exquisite Arhats portraits are painted on different artistic carriers.On the basis of inheriting the traditional Chinese paradigm painting expression,the portrait of Mount Wutai Arhats constantly draws on folk nutrients.It integrates foreign Buddhist culture for refinement and sublimation.The reconstructed portrait pays attention to the image expression of its objects and the shaping of spiritual temperament.The shape is ancient and vivid,and the scene is ethereal and mysterious,which rationally expresses the Buddhist philosophy of generosity,benevolence and kindness,and contains unique oriental aesthetics.

Camilla Guerrieri’s Portrait of Guidobaldo II della Rovere:Symbols of Honor and Power

The Altomani&Sons Collection owns a remarkable newly discovered portrait of Guidobaldo II della Rovere,Duke of Urbino(1514-1574),a historical military figure who was a condottiere,ruler of Urbino,Commander-in-chief of the Papal Estate,and Perfect of Rome,as well as a collector and patron of the Fine Arts.Camilla Guerrieri Nati(1628-1694),a seventeenth-century Italian painter from Fossombrone(in the province of Pesaro and Urbino),portrayed this heroic personage surrounded by emblems associated with his military courage and leadership,including his plumed burgonet helmet,metal gilded armor,a necklace with the golden fleece,and batons of secular and religious dominions.This oil painting on copper-considered a precious metal at the time-emphasizes the importance of the commission.The material and technique also reveals a unique artistic achievement in that it provides the painting with a smooth,reflective surface and vibrant coloration,symbolizing precious imagery.

Trust evaluation model of power terminal based on equipment portrait

As the number of power terminals continues to increase and their usage becomes more widespread,the security of power systems is under great threat.In response to the lack of effective trust evaluation methods for terminals,we propose a trust evaluation model based on equipment portraits for power terminals.First,we propose an exception evaluation method based on the network flow order and evaluate anomalous terminals by monitoring the external characteristics of network traffic.Second,we propose an exception evaluation method based on syntax and semantics.The key fields of each message are extracted,and the frequency of keywords in the message is statistically analyzed to obtain the keyword frequency and time-slot threshold for evaluating the status of the terminal.Thus,by combining the network flow order,syntax,and semantic analysis,an equipment portrait can be constructed to guarantee security of the power network terminals.We then propose a trust evaluation method based on an equipment portrait to calculate the trust values in real time.Finally,the experimental results of terminal anomaly detection show that the proposed model has a higher detection rate and lower false detection rate,as well as a higher real-time performance,which is more suitable for power terminals.

PP-GAN:Style Transfer from Korean Portraits to ID Photos Using Landmark Extractor with GAN

The objective of style transfer is to maintain the content of an image while transferring the style of another image.However,conventional methods face challenges in preserving facial features,especially in Korean portraits where elements like the“Gat”(a traditional Korean hat)are prevalent.This paper proposes a deep learning network designed to perform style transfer that includes the“Gat”while preserving the identity of the face.Unlike traditional style transfer techniques,the proposed method aims to preserve the texture,attire,and the“Gat”in the style image by employing image sharpening and face landmark,with the GAN.The color,texture,and intensity were extracted differently based on the characteristics of each block and layer of the pre-trained VGG-16,and only the necessary elements during training were preserved using a facial landmark mask.The head area was presented using the eyebrow area to transfer the“Gat”.Furthermore,the identity of the face was retained,and style correlation was considered based on the Gram matrix.To evaluate performance,we introduced a metric using PSNR and SSIM,with an emphasis on median values through new weightings for style transfer in Korean portraits.Additionally,we have conducted a survey that evaluated the content,style,and naturalness of the transferred results,and based on the assessment,we can confidently conclude that our method to maintain the integrity of content surpasses the previous research.Our approach,enriched by landmarks preservation and diverse loss functions,including those related to“Gat”,outperformed previous researches in facial identity preservation.

An Early Warning Model of Telecommunication Network Fraud Based on User Portrait

With the frequent occurrence of telecommunications and networkfraud crimes in recent years, new frauds have emerged one after another whichhas caused huge losses to the people. However, due to the lack of an effectivepreventive mechanism, the police are often in a passive position. Usingtechnologies such as web crawlers, feature engineering, deep learning, andartificial intelligence, this paper proposes a user portrait fraudwarning schemebased on Weibo public data. First, we perform preliminary screening andcleaning based on the keyword “defrauded” to obtain valid fraudulent userIdentity Documents (IDs). The basic information and account information ofthese users is user-labeled to achieve the purpose of distinguishing the typesof fraud. Secondly, through feature engineering technologies such as avatarrecognition, Artificial Intelligence (AI) sentiment analysis, data screening,and follower blogger type analysis, these pictures and texts will be abstractedinto user preferences and personality characteristics which integrate multidimensionalinformation to build user portraits. Third, deep neural networktraining is performed on the cube. 80% percent of the data is predicted basedon the N-way K-shot problem and used to train the model, and the remaining20% is used for model accuracy evaluation. Experiments have shown thatFew-short learning has higher accuracy compared with Long Short TermMemory (LSTM), Recurrent Neural Networks (RNN) and ConvolutionalNeural Network (CNN). On this basis, this paper develops a WeChat smallprogram for early warning of telecommunications network fraud based onuser portraits. When the user enters some personal information on the frontend, the back-end database can perform correlation analysis by itself, so as tomatch the most likely fraud types and give relevant early warning information.The fraud warning model is highly scaleable. The data of other Applications(APPs) can be extended to further improve the efficiency of anti-fraud whichhas extremely high public welfare value.

基于画像技术的教师研修路径智能推荐研究

教师是教育的第一资源,研修是促进教师专业发展的重要方式。在大数据、数字画像等新技术赋能下,优化教师研修路径以提升教师发展质量变得尤为重要。文章构建了多模态数据和画像技术支持的教师研修路径智能推荐模型,包括数据伴随式采集分类与预处理、教师画像生成、研修路径算法三个模块,实现教师研修特征与优质研修资源的智能匹配。在教师研修路径动态优化方面,模型通过提供基于画像的个性化导研服务、基于知识图谱的资源关联推荐、基于群体智能的群体路径发现、基于目标导向的过程评价和基于研修行为的智能预警,满足教师的个性化研修需求,为发掘研修数据潜能、促进教师智能研修模式创新提供参考。

基于用户画像的高校采购评审专家推荐算法

该文设计了一种可调参的混合推荐算法,融合AUC-MF算法与基于专家画像及项目画像的抽取算法,以提高高校随机抽取评审专家的专业匹配度,解决数据稀疏和冷启动问题。该文以某高校近6年政府采购项目历史评审数据以及项目文本信息构建数据集,对算法进行验证,该算法可有效缓解高校自选评审专家过程中的数据稀疏和冷启动问题,提高随机抽取专家的专业匹配度。

未来教育视域下高校新型混合式教学研究

未来教育是一种畅想和育人方向,既要保持教育的本真,也要紧跟时代的步伐,在技能与想象、工具性与价值性之间保持动态平衡。第四次工业革命诞生下的“互联网+”时代,教育衍生出多种学习和教学新形态。当今世界数字技术蓬勃兴起,生成式人工智能为高校教学带来机遇与挑战,混合式教学异军突起成为教学形式的主力军,也是未来教育教学的“新常态”表现之一。混合式教学一种特殊的社会生产也在走向内涵式发展,以强化教学互动多种实现方式为基石,以沉浸式教学环境为前提,以描摹数字画像促进多元评价为保障,不断探索混合式教学多种实现方式,积极重构教育新常态。

基于卷烟消费大数据标签体系的六大画像系统

为解决卷烟消费者数据采集与分析难问题,提出了一种全新的基于时空网格技术的消费人群大数据采集分析方法。通过商圈网格划分和数据标签特征工程,实现企业消费者大数据资源的高效融合,建立卷烟消费大数据标签体系,提高了数据的使用价值和复用性。基于大数据、时空网格、可视化技术构建卷烟市场数字孪生体,设计实现了六大画像系统。以玉林市场为例,实现了5237个商圈画像、19544个终端画像、42个品牌画像、169个产品画像以及12种商圈类别画像、24种卷烟分类画像。基于系统对某卷烟品牌实施精准投放实验,该产品的货源利用率从35%提升至59%。

多平台视角下用户知识交流主题挖掘与画像分析——以ChatGPT话题为例

[目的/意义]基于多平台视角挖掘用户知识交流主题特征,构建用户画像标签体系,有助于全面理解用户利用差异化平台开展知识交流行为的规律偏好,为平台提供精确优化策略,为平台间生态建设提供参考依据。[方法/过程]采集大众社交平台、兴趣交流平台、垂直知识平台3类典型平台中ChatGPT话题相关的原创博文及用户数据,采用BERTopic模型凝练知识交流主题,结合多平台数据特点,从自然属性、社会属性、知识交流行为属性和知识交流主题属性4个维度抽出画像标签,通过K-means聚类实现用户画像,呈现群组特征并进行平台差异对比。[结果/结论]研究识别出了应用场景、行业进展、未来探讨、相关产业、咨询求助、热门话题、使用感受、风险监督8大知识交流方向及46个主题;根据属性特征将用户划分为专业贡献型、综合共享型、社交求知型和话题潜力型4类,平台间知识交流主题和用户画像存在显著差异,各平台应采取差异化的激励方式,增强平台用户黏性。

人工智能视域下的医护科研人员知识服务

随着ChatGPT的兴起,如何将人工智能更好地运用于医护科研人员知识服务体系中,成为推进“健康中国”建设的重要举措。文章以医护科研人员为研究对象,以Wilson信息行为模型和信息搜寻行为的情境为理论基础,采用问卷调查法和因子分析法设计用户画像标签体系,构建医护科研人员用户画像。研究发现:医护科研人员可细分为科研成熟型、科研进阶型、科研起步型和科研潜在型四类群体;图书馆员可通过积极采纳AI服务理念进行用户特征的个性化与动态化识别,使用自然语言处理与机器学习实现医学资源自动化管理,以AI教学平台提升医护科研人员对新工具的掌握效率等途径,实现人工智能视域下医护科研人员知识服务效能的提升。

大数据环境下多模态融合的大学生异常行为预警

针对“校园大数据”累积的海量数据呈现出离散性、稀疏性等问题,如何从基数大、活动广、个性强的校园学生群体中检测出潜在的、有异常行为的学生,已成为学生异常行为分析亟需解决的问题.本文提出了一种大数据环境下基于多模态融合的大学生异常行为预警方法(early warning method for abnormal behavior of college students based on multi-modal fusion in big data environment,EWMAB).首先,针对学生行为画像的表征不够丰富,行为标签存在时效性、动态性等问题,建立一种基于多模态特征深度学习的跨模态学生行为画像模型;其次,针对学生异常行为预测、预警的时效性和后置性问题,在学生行为画像和学生行为分类预测基础上,提出了一种基于多模态融合的学生异常行为预警方法,通过长短期记忆神经网络(long and short term memory networks,LSTM),结合学生行为多指标数据和文本信息来解决学生异常行为预警问题;最后,本文通过应用实例验证模型以学生学习成绩异常预警为例,与其他预警算法相比,EWMAB方法可以提高预警的准确性,实现学生异常行为预警的时效性和前置性,从而使学生教育工作更具有针对性、个性化和预测性.

基于多维能力和知识图谱-多层感知机的变压器运行状态画像构建方法

利用大数据和画像技术对电力变压器运行状态进行准确评价有利于保障电力系统的安全稳定运行。针对电力变压器运行状态传统评价方法存在的评价维度过于单一、主观性较强等不足,提出了一种基于多维能力和知识图谱-多层感知机的变压器运行状态画像构建方法。首先,构建了由绝缘水平、负载能力、抗短路能力、能效等级和调压能力五个能力构成的变压器运行状态画像体系;然后,融合知识图谱(knowledge graph,KG)与多层感知机(multilayer perceptron,MLP),建立了一种变压器运行状态画像分析模型;最后,基于某地区1368台110kV变压器的实际运行数据,开展了变压器运行状态画像的实例分析,并与随机森林(random forest,RF)和支持向量机(support vector machine,SVM)方法的画像分析结果进行对比。研究结果表明,所提方法对变压器运行状态画像的准确率达到96.35%,优于RF算法(准确率89%)和SVM算法(准确率77%),为电力变压器的运行状态评价提供了一种新思路。

我国大城市居民参与互联网在线问诊的行为画像分析

目的/意义研究大城市居民选择互联网医疗服务时的典型特征,建立患者行为画像,为改进互联网医疗服务提供决策依据。方法/过程以北京市患者为研究对象,从个体属性特征、在线问诊行为以及择医需求偏好3个维度构建患者群体画像,通过聚类方法对在线患者群体分类。结果/结论在线患者可划分为价值潜力型、平台忠诚型、潜在转化型、需求强烈型和服务转移型5类不同特征群体。总体上来自大城市的在线患者病例级别偏低且活跃度不高,通常使用价格较低的图文问诊方式,倾向于选择来自三甲医院、职称较高且在线服务质量较高的医生。5类患者群体各具典型特征,需要分别采取针对性措施满足其个性化医疗需求。

高校纸本图书使用者的本科生用户画像构建

以高校纸本图书使用者的本科生用户为研究对象,构建高校纸本图书的用户画像,可以更加深入地识别用户、更加有效地提升纸本图书的资源建设和服务质量,进而提升用户体验,促进学生系统、深刻阅读。选取层级标签方法来构建用户画像,在现有RFM模型的基础上,构建LRFM模型,识别出具有共同特征的用户群体,最终完成用户画像的构建。基于真实数据,对该文构建的用户画像模型进行实证,验证模型是有效的。

基于精细时空行为测度的街道空间人群数字画像构建与分异

【目的】城市空间景观设计需要匹配多元化人群的实际活动需求,既有的数字技术方法更多从大尺度、宏观视角出发研究人群活动,难以满足景观尺度上对人群时空行为的智能画像研究。从景观-建筑尺度深入探讨面向街道设计的人群数字画像识别指标体系与测度方法,可为理解街道建成环境与人群分异之间的互动机制提供依据。【方法】以江苏省南京市中央路为案例,通过采集基于手机定位服务(location-based services,LBS)的人群时空数据和街道空间数据,运用人工智能算法分析不同类型人群的街道空间时空使用特征;提出包含基本属性、时间偏好、空间偏好、设施偏好和景观偏好等多维度指标的街道空间人群数字画像测度方法和谱系构建方法。【结果】识别出213种街道空间人群类型和7类典型人群数字画像,揭示了街道空间人群数字画像的时空分异特征,发现了这些特征与现状街道景观设计和设施布局的关联性。【结论】基于街道空间人群数字画像的分析结果,针对性地提出提升街道空间品质的规划建议,有助于更好地满足不同人群的实际活动需求,以助力城市空间景观设计响应居民需求,从而创造更加宜居、舒适的城市环境。

基于UTAUT模型修正的微信朋友圈信息流广告接受度实证研究

本文从解决微信朋友圈信息流广告接受度的现实困境出发,基于UTAUT模型进行修正构建了微信朋友圈信息流广告接受度SEM结构方程模型,重点研究影响微信朋友圈信息流广告的关键性因素及其相互关系。结果表明,绩效期望、付出期望、促成条件、社群影响、广告表现对微信朋友圈信息流广告接受度呈正相关,广告奖励对微信朋友圈信息流广告的接受意愿并不相关,微信使用用户的年龄越大,收入越高,对广告接受意愿越强。

多源数据融合下医护科研人员科研能力提升策略研究

目的/意义探讨医护科研人员用户画像构建方法和科研能力提升策略。方法/过程根据用户画像理论提出医护科研人员用户画像的构建模型;使用Python语言、ETL和Echarts工具对构建用户画像所需的多源异构数据进行采集、预处理、存储和挖掘,提取标签进行用户画像可视化并进行实证研究。结果/结论医护科研人员可分为具备科研潜质的护理人员、科研新手、科研提升型群体和科研领域资深专家,根据不同类型特征提出医护科研人员科研能力提升策略。

智能精准教研背景下教师群体画像实践研究

教师教育的发展是教育现代化的重要组成部分。基于精细化数据管理和科学决策的精准教研为教师专业发展提供了新的手段。该研究以教师群体画像技术为核心,通过构建群体画像框架,并以央馆智能研修平台所提供的数据为基础,聚焦于数学学科中“好课”的关键特征,并构建了教师群体画像标签体系。此外,通过对不同教师群体的职级、学科和学校等三维特征的分组,从常模分析、特征分析以及差异分析的角度揭示了教师群体的特征和课堂模式。研究表明,群体画像不仅有助于制定教师个性化成长方案,还能促进跨学科和跨校区的协同创新、推动数据驱动的教育决策,实现教育质量的持续改进和动态评估。