Integrate inconsistent and heterogeneous data based on user feedback

Purpose–Data integration is to combine data residing at different sources and to provide the users with a unified interface of these data.An important issue on data integration is the existence of conflicts among the different data sources.Data sources may conflict with each other at data level,which is defined as data inconsistency.The purpose of this paper is to aim at this problem and propose a solution for data inconsistency in data integration.Design/methodology/approach–A relational data model extended with data source quality criteria is first defined.Then based on the proposed data model,a data inconsistency solution strategy is provided.To accomplish the strategy,fuzzy multi-attribute decision-making(MADM)approach based on data source quality criteria is applied to obtain the results.Finally,users feedbacks strategies are proposed to optimize the result of fuzzy MADM approach as the final data inconsistent solution.Findings–To evaluate the proposed method,the data obtained from the sensors are extracted.Some experiments are designed and performed to explain the effectiveness of the proposed strategy.The results substantiate that the solution has a better performance than the other methods on correctness,time cost and stability indicators.Practical implications–Since the inconsistent data collected from the sensors are pervasive,the proposed method can solve this problem and correct the wrong choice to some extent.Originality/value–In this paper,for the first time the authors study the effect of users feedbacks on integration results aiming at the inconsistent data.

User Fairness Scheme with Proportional Fair Scheduling in Multi-user MIMO Limited Feedback System

In Multi-user MIMO (MU-MIMO) downlink system, suitable user selection schemes can improve spatial diversity gain. In most of previous studies, it is always assumed that the base station (BS) knows full channel state information (CSI) of each user, which does not consider the reality. However, there are only limited feedback bits in real system. Besides, user fairness is often ignored in most of current user selection schemes. To discuss the user fairness and limited feedback, in this paper, the user selection scheme with limited feedback bits is proposed. The BS utilizes codebook precoding transmitting strategy with LTE codebook. Furthermore, this paper analyzes the influence of the number of feedback bits and the number of users on user fairness and system sum capacity. Simulation results show that in order to achieve better user fairness, we can use fewer bits for feedback CSI when the number of user is small, and more feedback bits when the number of users is large.

Limited Feedback Bit Allocation for Cooperative Multi-cell Systems with Multi- user MIMO

For the cooperative multi-cell systems with multi-user MIMO, a new limited feedback bit allocation scheme is proposed to minimize the rate loss caused by quantization error. In the proposed scheme, the Channel State Information (CSI) feedback of cell-edge user for the local service cell and the adjacent interference cell are separately quantized. Based on the upper bound of the rate loss of cell-edge user due to the limited feedback, the number of feedback bits for quantized CSI of the local service cell and the adjacent cell are optimized with the fixed total bits of the limited feedback. The simulation shows that our proposed scheme of feedback bits allocation efficiently decreases the interference and increases the rate of systems compared with that of equal bits allocation and those of other allocations.

Developing Lexicons for Enhanced Sentiment Analysis in Software Engineering:An Innovative Multilingual Approach for Social Media Reviews

Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.

基于主客观数据的汽车人机交互界面表面触觉反馈方法

针对汽车用户界面虚拟按钮缺乏触觉反馈或触觉反馈感受单一的问题,采集3种真实物理按钮(切换开关、自锁按钮、复位按钮)数据并提取按钮特征,基于手指按压力大小分段实现了不同的振动反馈。提出一种重现按钮顺应性的触觉渲染方法,根据用户主观感受,验证了其相比于顺应性错觉方法在顺应性、用户愉悦性等方面的提升。进行触觉反馈匹配实验,实验者根据触觉感受将3种虚拟按钮与物理按钮类型进行匹配,准确率高达94.33%,验证了提出的虚拟按钮振动触觉反馈对不同物理按钮独特感知特性恢复的有效性,用户可以在无视觉需求的情况下辨别操纵的虚拟按钮类型和状态。

英国EV-elocity项目对我国V2G项目开展的借鉴与启示

近年来我国在电动汽车领域发展迅速,但当大量电动汽车接入电网时会对电网的稳定性和可靠性构成挑战,而V2G技术可以平衡电网峰谷差和促进新能源消纳,提高电网的灵活性,从而有效缓解大量电动汽车无序接入电网带来的问题。以英国的EV-elocity项目为案例,深入分析了V2G项目的环境价值、电池老化和用户反馈等方面,这些也是目前我国在开展V2G项目中关注相对较少的问题。此外,还简要介绍了我国V2G项目的实施情况,并指出我国V2G研究在项目开展和研究方向层面所面临的问题。最后,针对环境价值、电池老化、用户体验和接受度等方面提出了相关建议,这些建议旨在缩小与发达国家在该技术领域的差距,从而推动我国V2G项目的开展和V2G技术的应用。

基于隐式反馈和加权用户偏好的推荐算法

针对现有隐式反馈算法中正负样本划分不合理、忽略用户操作频次、无法准确建模用户偏好等问题,提出一种基于隐式反馈和加权用户偏好的推荐算法(IFW-LFM)。该算法考虑了用户操作频次与正负样本划分间的关系,学习并改进wALS算法,根据用户操作频次从缺失值中重新挖掘潜在正负样本,将用户操作频次大于1时的样本设置为正样本,用户操作频次为1或0时的样本为正样本或负样本,不再需要人为引入负样本;根据用户操作频次对用户偏好程度的影响,定义了置信度,明确用户偏好,并将其应用在隐因子模型的框架中;利用用户收听歌曲起止时间、收听时长等隐式反馈数据,提高隐式反馈样本利用度。在两个音乐数据集上的对比实验结果说明,该算法在准确率、召回率与NDCG值上与5个经典隐式反馈算法(UserCF、ItemCF、LFM、BPR、SVD)相比最大平均提升了45.81%,83.83%和60.33%,具有更优的推荐效果。

融入用户反馈的网络知识社区好友推荐模型构建研究

[目的/意义]从用户相似度计算和用户反馈双重视角出发,进行高质量用户推荐,有利于提高网络知识社区个性化推荐水平。[方法/过程]首先基于爬虫工具和Python包进行用户属性数据采集和分析,以计算用户之间的背景、社交关系、博文信息的综合相似度;然后再计算用户核心度,基于相似度矩阵和核心用户排名形成待推荐列表;最后引入用户反馈机制,并根据用户的正负反馈结果确定最佳Top-k好友。[结果/结论]融入用户反馈的好友推荐模型能够提高推荐用户的质量,有效地提高好友推荐水平,也验证了好友推荐是一个逐步修复的过程。

用户旅程中VOC对车企市场竞争力影响的实证研究

研究通过分析车企用户旅程中的反馈,探讨了VOC(Voice Of the Customer,用户声音)对产品开发、营销策略、用户体验和满意度的影响。研究采用定性和定量方法,识别了用户需求的关键点,并提出了基于用户中心的改进策略,证实了其在增强车企市场竞争力和用户满意度方面的有效性。

基于用户偏好的反馈式停车场车位推荐模型研究

为缓解城市停车难题,提升停车场智能化水平,提出了一种车位推荐算法,通过从用户和系统两个方面考虑,采用用户个人打分的AHP方法,充分考虑用户个性化偏好,同时加入占用率作为约束,兼顾系统均衡,为停车者提供停车位推荐方案,并可根据用户实际选择,引入多重约束条件,通过Lagrange乘数法,进行权重调整,提高算法准确性。本算法的应用将提升用户停车体验、改善场内交通拥堵、提高停车场运行效率和抗风险能力,提高路网交通韧性。

Angle-Based Beamforming in mmWave Massive MIMO Systems with Low Feedback Overhead Using Multi-Pattern Codebooks

The integration of millimeter-wave(mmWave)communications and massive multiple input multiple output(MIMO)techniques is a promising solution to dramatically increase the 5G network throughput.By using large antenna arrays,beamforming can be adopted to improve the 5G capacity by employing spatial domain resources.In a frequency division duplexing(FDD)based 5G mmWave MIMO system,beamforming operation requires timely downlink channel state information(CSI)feedback.However,the rapid channel variations caused by short wavelength of mmWave band,and the high-level feedback information required due to the large number of antennas in massive MIMO system lead to the significantly increased beamforming overhead.In this paper,by exploiting the higher angular stability of such channels,we propose an angle-based beamforming scheme to reduce the feedback frequency and the number of feedback bits.To facilitate this approach users are initially selected to reduce the intra-zone interference before beamforming.Besides,location related feedback,which is not affected by the number of antennas,is adopted to reduce overhead.The simulation results show that two proposed user selection algorithms can adapt to scenarios with diverse requirements,while the feedback overhead of proposed angle-based beamforming algorithm is sharply reduce compared with that of CSIbased beamforming algorithm.

Joint Source and Relay Precoding/Beamforming Algorithm for OFDM Multi-user Cooperative Relay Systems

In order to reduce the feedback load of multi-user orthogonal frequency division multiplexing （ OFDM ） -based wireless systems, a practiral limited bits feedback precoding algorithm is proposed with direct source-destination link based on amplify-and- forward cooperative relay network under frequency selective fading channels. Using joint minimum mean square error（MMSE） filter, the receiving decoding matrix is designed for each user in the paper. Source precoding （beamforming） matrix is optimized with convex function of weight mean square error （MSE）. Relay precoding matrix is obtained under MSE decomposition and convex optimization. The precoding matrix index is fed back for clustered subcarrier of OFDM with limited feedback. Then using interpolation algorithm, all precoding matrices are achieved at base station （BS） and relay nodes. Simulations indicate the effectiveness of the proposed limited feedback joint precoding and beam_formlng design. The proposed method can improve bit error rate （BER） performance and obtain better sum-rate performance in contrast to existing algorithms. It displays the BER performance is close to that of the unquantified precoding feedback method.

Information perception and feedback mechanism and key techniques of multi-modality human-robot interaction for service robots

With the increasing of the elderly population and the growing hearth care cost, the role of service robots in aiding the disabled and the elderly is becoming important. Many researchers in the world have paid much attention to heaRthcare robots and rehabilitation robots. To get natural and harmonious communication between the user and a service robot, the information perception/feedback ability, and interaction ability for service robots become more important in many key issues.

基于文本挖掘的政府数据开放平台在线评论内容特征分析

政府开放数据平台是连接用户和数据的重要桥梁。文章从用户利用的视角出发,采用Tf-idf特征提取、词云图、社会语义网络图等深度挖掘文本内容,展示用户需求的热门数据并及时找到存在的问题,从而提出优化内容服务、增强互动设计、完善反馈解答以及规范隐私保护四个方面的建议。通过对政府数据开放平台的用户反馈评论数据的挖掘,为我国政府开放数据平台的发展提供参考建议。

假肢交互中振动反馈的设计方法

目的 完成假肢交互中振动反馈的设计方法,选择合适的振动反馈信号,为假肢反馈端的设计提供科学依据。方法 选择振动作为反馈信号,通过人机交互实验测量不同振动强度下人体的差别阈限,模拟截肢者在振动反馈下使用假肢完成任务的过程。结果 得出相应的用户数据并对其进行分析,在实验结果基础上提出假肢交互中振动反馈的设计方法。结论 振动对于人体是一种新的信号,截肢者可以通过训练逐渐掌握振动反馈,提升其对假肢产品的认同感,将假肢真正作为自己身体的一部分。以振动反馈的设计流程为例所提出的振动反馈的设计方法,为假肢产品反馈端的信号选择提供了包括用户灵敏度、人体对交互方式的适应期和用户的心理学数据等量化信息,提升截肢者对假肢产品的体验。

一种面向用户反馈的智能分析与服务设计方法

针对用户评论数据,提出了一种面向用户反馈的智能分析与服务设计方法。该方法选取了IOS平台多个App的用户评论数据,对其进行智能挖掘和分类,分析其中的潜在需求。首先,分析用户需求类别,将划分的10个需求进行具体定义。其次,对用户数据进行爬取、清洗和标注,形成软件分类数据集。通过实验检验TextCNN、BiLSTM_Attention和BERT对用户评论数据智能分类的效果,将分类结果进行优先级排序。最后,将该方法封装成一种可重用的智能服务供使用者远程调用。实验结果表明:TextCNN模型综合效果最好,在单一指标Precision上,BERT模型效果最好;BERT模型利用并行计算优化训练过程,使其可拓展到大规模项目,在数据量大、精确性要求比较高的情况下,推荐BERT模型;反之,在应对数据小、时限紧的情况时,推荐TextCNN模型。

融合用户感知和多因素的兴趣点推荐

针对传统的基于协同过滤的兴趣点(POI)推荐方法存在数据稀疏问题和现有工作往往单纯利用上下文信息却没有合理平衡各因素的作用影响的问题,提出融合用户感知和多因素的兴趣点推荐模型(UPMF).为基于用户感知的隐式建模提取用户相似性以丰富用户表示,并利用序列、地理和社交等上下文信息构建用户感知协同影响的POI推荐模型,缓解数据稀疏问题.设计新颖的用户感知的融合策略(UPIS),在基于用户感知的同时合理利用各种上下文信息挖掘用户的动态偏好.提出基于分段的活动区域选择算法针对不同活动区域对用户的影响进行建模.实验结果表明,与其他流行的POI推荐方法相比,UPMF在准确率、召回率和归一化折损累计增益(NDCG) 3个评价标准上都有一定程度的提高.在Gowalla和Yelp数据集上,UPMF模型的NDCG@10比SUCP的分别高12.77%、7.24%.

基于5G通讯下新型智能电表箱的设计研究

在智能电表的众多模块中,晶振模块是其中最重要的模块。大多数硬件工程师都熟悉振荡器,但真正了解共诺原理的寥寥无几,更不用说如何正确设计晶体振荡器电路模块。在智能电表设计实践中,硬件设计人员甚至没有真正关注振荡器设计,直到他们意识到振荡器无法正常运行。许多开发项目因为晶振电路模块没设计好而导致项目延期。

社群情境下在线评价反馈对消费者购买意愿的影响——以XHS为例

本文从消费者在线评价反馈角度出发,结合文献回顾提出研究框架,使用问卷调查分析在线评价反馈对消费者购买意愿的影响。通过构建模型进行实证研究,分析在线评价反馈、用户黏性、消费者购买意愿三者之间的关系,为XHS平台的未来发展提供可行性的建议。结论发现:在线评价来源对消费者购买意愿有着正向影响,而用户黏性在线评价反馈对消费者购买意愿影响存在正向调节作用。

基于智能反射面与用户关联的1比特反馈波束赋形算法

针对多智能反射面辅助的多接入点多用户系统中的智能反射面选择与主被动联合波束赋形问题,设计了不依赖瞬时信道状态信息的智能反射面-用户关联与波束赋形方案。首先基于大尺度衰落参数设计关联方案。之后提出了基于1比特反馈的被动波束赋形算法,利用接收信号强度调整反射单元的相位。仿真结果表明用户关联方案对平均信干噪比存在显著影响,收敛后的1比特反馈算法能够接近主被动波束赋形相位对齐时的性能。