Online composite shape recognition based on relevance feedback

This paper describes a novel method of online composite shape recognition interms of the relevance feedback technology to capture a user's intentions incrementally, and adynamic user modeling method to adapt to various users' styles. First, the relevance feedback isadapted to refine the recognition results and reduce the ambiguity incrementally based on theestablishment of a feature-based vector model of a user's sketches. Secondly, a dynamic usermodeling is introduced to model the user's sketching habits based on recording and analyzinghistorical information incrementally. A model-based matching strategy is also employed in the methodto recognize sketches dynamically. Experiments prove that the proposed method is both effective andefficient.

User’s Relevance of PIR System Based on Cloud Models

A new method to evaluate fuzzily user＇s relevance on the basis of cloud models has been proposed. All factors of personalized information retrieval system are taken into account in this method. So using this method for personalized information retrieval （PIR） system can efficiently judge multi-value relevance, such as quite relevant, comparatively relevant, commonly relevant, basically relevant and completely non-relevant, and realize a kind of transform of qualitative concepts and quantity and improve accuracy of relevance judgements in PIR system. Experimental data showed that the method is practical and valid. Evaluation results are more accurate and approach to the fact better.

Image Semantic Automatic Annotation by Relevance Feedback

A large semantic gap exists between content based index retrieval(CBIR) and high-level semantic,additional semantic information should be attached to the images,it refers in three respects including semantic representation model,semantic information building and semantic retrieval techniques.In this paper,we introduce an associated semantic network and an automatic semantic annotation system.In the system,a semantic network model is employed as the semantic representation model,it uses semantic Key words,linguistic ontology and low-level features in semantic similarity calculating.Through several times of users' relevance feedback,semantic network is enriched automatically.To speed up the growth of semantic network and get a balance annotation,semantic seeds and semantic loners are employed especially.

Method of Relevance Judgment for App Software’s User Reviews

In order to judge whether the user reviews are relevant to App software, this paper proposed a method to judge the relevance of user reviews based on Naive Bayesian text classification and term frequency.Firstly, the keywords sets of App software’s user reviews are extracted. Then, the keywords sets are optimized. Finally, the relevance score of the user reviews are calculated, and whether the user reviews are relevant is judged. Through the experiment, this method is proved that can judge the relevance of App software’s user reviews effectively.

An Improved Asymmetric Bagging Relevance Feedback Strategy for Medical Image Retrieval

Much attention has been paid to relevant feedback in intelligent computation for social computing, especially in content-based image retrieval which based on WeChat platform for the medical auxiliary. It has a good effect on reducing the semantic gap between high semantics and low semantics of images. There are many kinds of support vector machines (SVM) based relevance feedback methods in image retrieval, but all of them may encounter some problems, such as a small size of sample, an asymmetric positive sample and negative sample as well as a long feedback cycle. To deal with these problems, an improved asymmetric bagging (IAB) relevance feedback algorithm is proposed. Furthermore, we apply a new fuzzy support machine (FSVM) to cooperate with IAB. To solve the over-fitting and real-time problems, we use modified local binary patterns (MLBP) as image features. Finally, experimental results demonstrate that our method performs other methods in terms of improving retrieval precision as well as retrieval efficiency.

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.

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.

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.

Analysis and Simulations of Open-Source Intelligence Process System Dynamics from User’s Perspective

In today’s society with advanced Internet,the amount of information increases dramatically with each passing day,which leads to increasingly complex processes of open-source intelligence.Therefore,it is more important to rationalize the operation mode and improve the operation efficiency of open-source intelligence under the premise of satisfying users’needs.This paper focuses on the simulation study of the process system of opensource intelligence from the user’s perspective.First,the basic concept and development status of open-source intelligence are introduced in details.Second,six existing intelligence operation process models are summarized and their advantages and disadvantages are compared in focus.Based on users’preference,the open-source intelligence system simulation theory model is constructed from four aspects:intelligence collection,intelligence processing,intelligence analysis,and intelligence delivery.Meanwhile,the dynamics model of the open-source intelligence process system is constructed based on the open-source intelligence system simulation theoretical model,which specifically includes five parts:determination of system boundary,construction of causal loop diagram,construction of stock flow diagram,writing ofmathematical equations,and system sensitivity test.Finally,the system simulation results were analyzed.It was found that improving the system of intelligence agencies,opening up government affairs,improving the professional level of intelligence personnel,strengthening the communication and cooperation among personnel of various intelligence departments,and expressing intelligence products through diverse forms can effectively improve the operational efficiency of the open-source intelligence process system.

An Intelligent IEQ Monitoring and Feedback System:Development and Applications

Indoor environmental quality(IEQ)significantly affects human health and wellbeing.Therefore,continuous IEQ monitoring and feedback is of great concern in both the industrial and academic communities.However,most existing studies only focus on developing sensors that cost-effectively promote IEQ measurement while ignoring interactions between the human side and IEQ monitoring.In this study,an intelligent IEQ monitoring and feedback system-the Intelligent Built Enviroment(IBEM)-is developed.Firstly,the IBEM hardware instrument integrates air temperature,relative humidity,CO_(2),particulate matter with an aerodynamic diameter no greater than _(2.5)μm(PM_(2.5)),and illuminance sensors within a small device.The accuracy of this integrated device was tested through a co-location experiment with reference sensors;the device exhibited a strong correlation with the reference sensors,with a slight deviation(R^(2)>0.97 and slopes between 1.01 and 1.05).Secondly,a wireless data transmission module,a cloud storage module,and graphical user interfaces(i.e.,a web platform and mobile interface)were built to establish a pathway for dataflow and interactive feedback with the occupants of the indoor environments.Thus,the IEQ parameters can be continuously monitored with a high spatiotemporal resolution,interactive feedback can be induced,and synchronous data collection on occupant satisfaction and objective environmental parameters can be realized.IBEM has been widely applied in 131 buildings in 18cities/areas in China,with 1188 sample locations.Among these applications,we report on the targeted IEQ diagnoses of two individual buildings and the exploration of relationships between subjective and objective IEQ data in detail here.This work demonstrates the great value of IBEM in both industrial and academic research.

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.

User space transformation in deep learning based recommendation

Deep learning based recommendation methods, such as the recurrent neural network based recommendation method(RNNRec) and the gated recurrent unit(GRU) based recommendation method(GRURec), are proposed to solve the problem of time heterogeneous feedback recommendation. These methods out-perform several state-of-the-art methods. However, in RNNRec and GRURec, action vectors and item vectors are shared among users. The different meanings of the same action for different users are not considered. Similarly, different user preference for the same item is also ignored. To address this problem, the models of RNNRec and GRURec are modified in this paper. In the proposed methods, action vectors and item vectors are transformed into the user space for each user firstly, and then the transformed vectors are fed into the original neural networks of RNNRec and GRURec. The transformed action vectors and item vectors represent the user specified meaning of actions and the preference for items, which makes the proposed method obtain more accurate recommendation results. The experimental results on two real-life datasets indicate that the proposed method outperforms RNNRec and GRURec as well as other state-of-the-art approaches in most cases.

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。

双重绩效反馈相关性对企业异质性创新战略抉择的影响研究

绩效反馈对组织创新的影响一直备受关注,然而历史绩效和行业绩效连续时期的反馈相关性对企业异质性创新战略抉择的影响机理尚未厘清。基于绩效反馈理论,以2010—2022年中国深沪A股高科技制造业上市公司为样本,探讨双重绩效反馈相关性对企业异质性创新战略抉择的影响机理,以及高管股权激励的调节效应。研究结果显示,双重绩效反馈相关性与企业突破性创新呈U型关系,双重绩效反馈相关性与企业渐进性创新呈倒U型关系。高管股权激励强化了双重绩效反馈强相关与企业突破性创新之间的关系,也强化了弱相关与企业渐进性创新之间的关系。

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。

基于相似性增强传播结构的谣言检测

社交媒体的快速崛起引发了谣言传播问题,对社会造成负面影响。现有的谣言检测算法主要关注新闻内容和传播结构,却往往忽视了用户偏好的相似性可能带来的潜在影响。在浏览帖文时,用户更容易接触到与自己具有相似偏好的其他用户所传播的信息,从而助长谣言的传播。此外,现有研究常常忽视了传播结构的多样性,也忽视了新闻内容与其传播结构之间的关联。不同类型的新闻应具备不同的传播模式。因此,本文提出了一种名为“SEPS”的模型,旨在在偏好相似用户之间建立联系,再将传播结构划分为多种形式,以提取不同传播模式的特征。最后,引入对比学习和共注意力模块,增强了新闻内容和传播结构之间的相关性。实验证明,“SEPS”模型能有效检测谣言,其性能超越了最佳基线模型。

基于数据驱动模型的反窃电行为精准取证系统

反窃电行为现场蹲守取证的时效性与准确性均较差,导致取证工作效率下降,且取得的证据不完整,设计基于数据驱动模型的反窃电行为精准取证系统。在现场布设反窃电稽查仪、红外摄像头,在采集层获取用户相关用电数据以及视频图像,并通过通信层的集中器将数据传输到主站层。主站层借助数据驱动模型从用电数据以及视频图像中挖掘证据,对获取的证据进行实时整理和归纳,完成反窃电行为精准取证。实验结果表明,利用所设计系统对窃电用户进行检测与识别后所得F1值均大于9,且接近于10,能够实现反窃电行为精准取证。

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。

白族家具文化交互叙事传播研究

在国家对优秀传统文化信息共享计划实施政策背景推动下,少数民族家具文化传播方式发生了根本改变。重点研究白族家具文化特色,提炼白族家具文化价值,从文化传播角度分析传播模式及传播要素的关系,总结出白族家具文化传播要素关系,为提升和优化大众对白族家具文化的认知和体验感,基于“5W”模式传播要素的关系特点,结合交互叙事理论,探索白族家具文化传播信息框架,构建白族家具文化交互叙事传播模型,提出白族家具文化传播实践方法,最后通过白族家具App设计案例进行论证,通过问卷调查收集用户评价和反馈,从而获取优化文化传播关键信息,对少数民族家具文化传播和探索现代社会民族文化传播新路径提供参考。

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.