Research on Accurate Information Pushing Based on Human Network

Based on the social network analysis methods and human network, this paper randomly selected 44 students (31 males and 13 females) as the research objects, and it used the UCINET software to analyze the friendship between them of which 43 used WeChat and 44 used QQ, and it also used the tool Netdraw to visualize the network sociogram. By mining the four aspects of density, accessibility, centrality, block model, the results demonstrated that QQ social network and WeChat social network existed the phenomenon of small world, leaders and subgroups, and the key nodes of QQ human network were more than WeChat network. Through using the key nodes, it can push the precise and efficient information and improve the accuracy of information transmission and impact among network members.

Starants: A New Model for Human Networks

In this paper, we will explain the relevance of the starant graphs, graphs created by us in the year of 2002. They were basically circulant graphs with a star graph that connects to all the vertices of the circulant graphs from inside of them, but they did not exist as a separate object of study in the year of 2002, as for all we knew. We now know that they can be used to model even social networking interactions, and they do that job better than any other graph we could be trying to use there. With the development of our mathematical tools, lots of conclusions will be made much more believable and therefore will become much more likely to get support from the relevant industries when attached to new queries.

Expert recommendation system based on analyzing expertise and networks of human resources in National Science & Technology Information Service

This work aims to implement expert and collaborative group recommendation services through an analysis of expertise and network relations NTIS. First of all, expertise database has been constructed by extracting keywords after indexing national R＆D information in Korea （human resources, project and outcome） and applying expertise calculation algorithm. In consideration of the characteristics of national R＆D information, weight values have been selected. Then, expertise points were calculated by applying weighted values. In addition, joint research and collaborative relations were implemented in a knowledge map format through network analysis using national R＆D information.

Identification of TSS in the Human Genome Based on a RBF Neural Network

The identification of functional motifs in a DNA sequence is fundamentally a statistical pattern recognition problem. This paper introduces a new algorithm for the recognition of functional transcription start sites （TSSs） in human genome sequences, in which a RBF neural network is adopted, and an improved heuristic method for a 5-tuple feature viable construction, is proposed and implemented in two RBFPromoter and ImpRBFPromoter packages developed in Visual C＋＋ 6.0. The algorithm is evaluated on several different test sequence sets. Compared with several other promoter recognition programs, this algorithm is proved to be more flexible, with stronger learning ability and higher accuracy.

RETRACTED:<i>Realization of Virtual Human Face Based on Deep Convolutional Generative Adversarial Networks</i>

Short Retraction Notice The authors claim that this paper needs modifications. This article has been retracted to straighten the academic record. In making this decision the Editorial Board follows COPE's Retraction Guidelines. The aim is to promote the circulation of scientific research by offering an ideal research publication platform with due consideration of internationally accepted standards on publication ethics. The Editorial Board would like to extend its sincere apologies for any inconvenience this retraction may have caused. Editor guiding this retraction: Prof. Baozong Yuan(EiC of JSIP) The full retraction notice in PDF is preceding the original paper, which is marked "RETRACTED".

Human Detection by Robotic Urban Search and Rescue Using Image Processing and Neural Networks

This paper proposes a new approach for detecting human survivors in destructed environments using an autonomous robot. The proposed system uses a passive infrared sensor to detect the existence of living humans and a low-cost camera to acquire snapshots of the scene. The images are fed into a feed-forward neural network, trained to detect the existence of a human body or part of it within an obstructed environment. This approach requires a relatively small number of images to be acquired and processed during the rescue operation, which considerably reduces the cost of image processing, data transmission, and power consumption. The results of the conducted experiments demonstrated that this system has the potential to achieve high performance in detecting living humans in obstructed environments relatively quickly and cost-effectively. The detection accuracy ranged between 79% and 91% depending on a number of factors such as the body position, the light intensity, and the relative color matching between the body and the surrounding environment.

An EEGA-Based Bayesian Belief Network Model for Recognition of Human Activity in Smart Home

With the emerging of sensor networks, research on sensor-based activity recognition has attracted much attention. Many existing methods cannot well deal with the cases that contain hundreds of sensors and their recognition accuracy is requisite to be further improved. A novel framework for recognizing human activities in smart home was presented. First, small, easy-to-install, and low-cost state change sensors were adopted for recording state change or use of the objects. Then the Bayesian belief network (BBN) was applied to conducting activity recognition by modeling statistical dependencies between sensor data and human activity. An edge-encode genetic algorithm (EEGA) approach was proposed to resolve the difficulties in structure learning of the BBN model under a high dimension space and large data set. Finally, some experiments were made using one publicly available dataset. The experimental results show that the EEGA algorithm is effective and efficient in learning the BBN structure and outperforms the conventional approaches. By conducting human activity recognition based on the testing samples, the BBN is effective to conduct human activity recognition and outperforms the naive Bayesian network (NBN) and multiclass naive Bayes classifier (MNBC).

Digital Watermarking Algorithm Based on Wavelet Transform and Neural Network

An effective blind digital watermarking algorithm based on neural networks in the wavelet domain is presented. Firstly, the host image is decomposed through wavelet transform. The significant coefficients of wavelet are selected according to the human visual system （HVS） characteristics. Watermark bits are added to them. And then effectively cooperates neural networks to learn the characteristics of the embedded watermark related to them. Because of the learning and adaptive capabilities of neural networks, the trained neural networks almost exactly recover the watermark from the watermarked image. Experimental results and comparisons with other techniques prove the effectiveness of the new algorithm.

Accurate and Robust Eye Center Localization via Fully Convolutional Networks

Eye center localization is one of the most crucial and basic requirements for some human-computer interaction applications such as eye gaze estimation and eye tracking. There is a large body of works on this topic in recent years, but the accuracy still needs to be improved due to challenges in appearance such as the high variability of shapes, lighting conditions, viewing angles and possible occlusions. To address these problems and limitations, we propose a novel approach in this paper for the eye center localization with a fully convolutional network(FCN),which is an end-to-end and pixels-to-pixels network and can locate the eye center accurately. The key idea is to apply the FCN from the object semantic segmentation task to the eye center localization task since the problem of eye center localization can be regarded as a special semantic segmentation problem. We adapt contemporary FCN into a shallow structure with a large kernel convolutional block and transfer their performance from semantic segmentation to the eye center localization task by fine-tuning.Extensive experiments show that the proposed method outperforms the state-of-the-art methods in both accuracy and reliability of eye center localization. The proposed method has achieved a large performance improvement on the most challenging database and it thus provides a promising solution to some challenging applications.

Assessing the impacts of human activities and climate variations on grassland productivity by partial least squares structural equation modeling(PLS-SEM)

The cause-effect associations between geographical phenomena are an important focus in ecological research. Recent studies in structural equation modeling（SEM） demonstrated the potential for analyzing such associations. We applied the variance-based partial least squares SEM（PLS-SEM） and geographically-weighted regression（GWR） modeling to assess the human-climate impact on grassland productivity represented by above-ground biomass（AGB）. The human and climate factors and their interaction were taken to explain the AGB variance by a PLS-SEM developed for the grassland ecosystem in Inner Mongolia, China. Results indicated that 65.5% of the AGB variance could be explained by the human and climate factors and their interaction. The case study showed that the human and climate factors imposed a significant and negative impact on the AGB and that their interaction alleviated to some extent the threat from the intensified human-climate pressure. The alleviation may be attributable to vegetation adaptation to high human-climate stresses, to human adaptation to climate conditions or/and to recent vegetation restoration programs in the highly degraded areas. Furthermore, the AGB response to the human and climate factors modeled by GWR exhibited significant spatial variations. This study demonstrated that the combination of PLS-SEM and GWR model is feasible to investigate the cause-effect relation in socio-ecological systems.

基于人水和谐视角的国家水网优化布局构想与展望

国家水网建设有助于缓解当前日益复杂的人水矛盾,基于人水和谐论开展国家水网优化布局研究,能够为国家水网建设提供新的思路。本文从人水和谐的视角出发,通过梳理国家水网提出过程,分析现阶段国家水网建设中亟待解决的问题,在此基础上辨析人水和谐论对国家水网优化布局的指导作用;进一步以人水和谐理念为引领,从指导理论、基本原则、布局方法、关键内容与研究途径等方面提出了国家水网优化布局的构建框架;从促进人与自然和谐共生、实现综合效益最优发挥、支撑国家安全总体保障、助力先进文化繁荣发展、建设现代水网智慧服务5个方面描绘了国家水网优化布局的蓝图;从理论、方法、技术、支撑体系4个方面对国家水网优化布局未来研究进行展望。

面向新质生产力发展需求的国家水网建设关键内容及研究展望

为推动国家水网建设稳步迈入新质生产力发展的新征程,在理解水利新质生产力内涵及发展需求的基础上,从高科技、高效能、高质量、绿色4个维度出发,系统梳理国家水网建设需要把握的关键内容,分析有代表性的研究实例,并在此基础上进一步对国家水网未来研究进行展望,包括研发国家水网模拟器大科学装置、物理水网和虚拟水网协同建设、支撑人与自然和谐共生的现代化建设、科学处理人水关系4个方面,以期为面向新质生产力发展的国家水网建设提供参考。

数字人文视域下20世纪留美作曲家社会网络关系分析

20世纪留美作曲家群体为近现代以来中国音乐的发展做出了不可替代的贡献。文章从数字人文角度出发,梳理多方数据来源,利用社会网络分析软件Ucinet构建20世纪留美作曲家群体社会关系网络,计算网络特征指标,并从师承关系、校友关系、同乡关系三个方面剖析了留美作曲家社会网络关系,具有多重发现,为中国近代音乐史、留学史以及作曲家群体研究提供了新的视角与方法。

改进贝叶斯网络模型在起重作业人机交互差错风险分析中的应用

为量化分析起重作业人机交互差错风险,根据安全工效学原理及安全技术规范将起重作业人、机、环相关影响因素作为根节点,按照事故致因层次关联关系确定子节点,构建起重作业人机交互差错的3层级贝叶斯网络模型(Bayesian Network, BN);基于模糊集理论,采用认知可靠性与失误分析方法(Cognitive Reliability and Error Analysis Method, CREAM),厘定贝叶斯网络父节点失效概率以及中间节点条件概率;利用逆向推理仿真技术分析起重作业人机交互差错发生的因果链,探究起重伤害事故发生的人机交互差错风险。结果表明:起重作业人机交互差错最可能致因链为起重设备安全检查不到位→管理人员失误→人员操作失误→起重伤害事故发生;单因素失效条件下,起重作业人机交互差错风险概率呈线性增长趋势;在多因素失效条件下,一级节点因素失效概率愈大则人机交互差错效应愈显著,且呈现非线性增长态势。

基于多流语义图卷积网络的人体行为识别

与基于图像的行为识别方法相比,利用人体骨架信息进行识别能有效克服复杂背景、光照变化以及外貌变化等因素影响。但是,目前主流的基于人体骨架的行为识别方法存在参数量过大、运算速度慢等问题。对此,提出一种多流轻量级语义图卷积的行为识别方法。设计多流语义引导的图卷积网络(MS-SGN),将骨架信息分别表达为骨长流、关节流和细粒度流3种数据流形式,再对嵌入语义信息的数据流通过自适应图卷积提取空间特征,并采用不同内核和膨胀率的多尺度时域卷积提取时域特征,最后对各流分类结果进行加权融合。实验结果表明,该方法在NTU60 RGB+D数据集上的识别精度分别为90.0%(X-Sub)和95.83%(X-View),在NTU120 RGB+D数据集上的识别精度分别为83.4%(X-Sub)和84.0%(X-View),优于SGN、Logsin-RNN等主流方法,且网络框架更为轻量化。

A WBAN for Human Movement Kinematics and ECG Measurements

Biomedical applications of body area networks (BANs) are evolving, where taking periodic medical readings of patients via means wireless technologies at home or in the office will aid physicians to periodically supervise the patient’s medical status without having to see the patient. Thus, one important objective of BANs is to provide the doctor with the medical readings that can be collected electronically without being in close proximity to the patient. This is done through the measurement of the patient’s physiological signals via means of wearable sensors. This paper investigates wireless BAN cooperation via actual measurements of human movement kinematics and electrocardiogram (ECG), which are believed to provide patients with easy healthcare for continuous health-monitoring. The collected information will be processed using specially designed software, which in turn will enable the patient to send a full medical chart to the physician’s electronic device. In this way, physicians will have the ability to monitor their patients more efficiently.

全球创新网络嵌入、人力资本结构与企业创新绩效

技术创新模式正由传统的线性和链式形态向多元主体共享合作的网络模式转变,企业创新的机遇与挑战并存。为此,本文基于熊彼特创新理论和新经济地理学的建模思想,构建了用于讨论全球创新网络嵌入、人力资本结构调整与企业创新绩效提升之间微观互动机制的理论模型,并运用2011—2019年上市公司数据对此进行实证检验。研究发现:企业嵌入全球创新网络的决策受人力资本结构的影响,二者与企业创新绩效间存在正向的循环累积效应;人力资本结构存在最优配置点,能够最大限度地激发企业创新绩效;地理区位、本地创新资源禀赋和企业所有制结构等外部市场环境也会影响企业的全球化进程和创新水平。研究表明,以全球视野谋划和推动科技创新,引导企业融入全球科技创新网络,协同人才结构优化策略,开创科技发展新格局,不仅是新时代我国企业提升国际竞争力的重要途径,也是建设科技强国的关键举措。

On the trails of markers and proxies:the socio-cognitive technologies of human movement,knowledge assemblage,and their relevance to the etiology of nasopharyngeal carcinoma

Bacteria,pigs,rats,pots,plants,words,bones,stones,earrings,diseases,and genetic indicators of all varieties are markers and proxies for the complexity of interweaving trails and stories integral to understanding human movement and knowledge assemblage in Southeast Asia and around the world.Understanding human movement and knowledge assemblage is central to comprehending the genetic basis of disease,especially of a cancer like nasopharyngeal carcinoma.The problem is that the markers and trails,taken in isolation,do not all tell the same story.Human movement and knowledge assemblage are in constant interaction in an adaptive process of co-production with genes,terrain,climate,sea level changes,kinship relations,diet,materials,food and transport technologies,social and cognitive technologies,and knowledge strategies and transmission.Nasopharyngeal carcinoma is the outcome of an adaptive process involving physical,social,and genetic components.