Consensus model of social network group decision-making based on trust relationship among experts and expert reliability

Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making.This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making(SN-GDM).A concept named matching degree is proposed to measure expert reliability.Meanwhile,linguistic information is applied to manage the imprecise and vague information.Matching degree is expressed by a 2-tuple linguistic model,and experts’preferences are measured by a probabilistic linguistic term set(PLTS).Subsequently,a hybrid weight is explored to weigh experts’importance in a group.Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus.Finally,a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.

Synchronization investigation of the network group constituted by the nearest neighbor networks under inner and outer synchronous couplings

A new synchronization technique of inner and outer couplings is proposed in this work to investigate the synchro- nization of network group. Some Haken-Lorenz lasers with chaos behaviors are taken as the nodes to construct a few nearest neighbor complex networks and those sub-networks are also connected to form a network group. The effective node controllers are designed based on Lyapunov function and the complete synchronization among the sub-networks is realized perfectly under inner and outer couplings. The work is of potential applications in the cooperation output of lasers and the communication network.

Network Group Psychological Education of College Students

Based on the perspective of psychology,this paper analyzes the causes and characteristics of college students’network mass incidents,explores the psychological factors of college students’network mass incidents,and puts forward the educational strategies to solve college students’network mass incidents:(1)Adhere to humanism and take appeals as the center;(2)To improve the campus network public opinion guidance mechanism under the guidance of relevant social cognition theories;(3)Strengthen communication and improve communication skills;(4)Promote information disclosure and transparency,and eliminate uncertainty and ambiguity.

Network Group Psychological Education of College Students

Based on the perspective of psychology,this paper analyzes the causes and characteristics of college students’network mass incidents,explores the psychological factors of college students’network mass incidents,and puts forward the educational strategies to solve college students’network mass incidents:No.1.Adhere to humanism and take appeals as the center;No.2.To improve the campus network public opinion guidance mechanism under the guidance of relevant social cognition theories;No.3.Strengthen communication and improve communication skills;No.4.Promote information disclosure and transparency,and eliminate uncertainty and ambiguity.

基于Group-Res2Block的智能合成语音说话人确认方法

针对现有说话人确认任务基于自然语音条件下并不适用于智能合成语音的问题,提出一种基于Group-Res2Block的智能合成语音说话人确认方法。首先,设计了Group-Res2Block结构,在Res2Block的基础上将当前分组与相邻前后分组进行合并形成新的分组,以增强说话人局部特征的上下文联系;其次,设计了并行结构的多尺度通道注意力特征融合机制,利用不同大小卷积核实现同一层级的特征在通道维度的特征选择,以获取更具表现力的说话人特征,避免信息冗余;最后,设计了串行结构的多尺度层注意力特征融合机制,构建层结构,将深浅层特征整体进行融合并赋予不同权重,以获取最优的特征表达。为验证所提出特征提取网络的有效性,构建了中英文两种智能合成语音数据集进行消融实验和对比实验。结果表明本文方法在该任务的评价指标精确度(accuracy,ACC)、等错误率(equal error rate,EER)和最小检测代价函数(minimum detection cost function,minDCF)上是最优的。此外,通过对模型泛化性能进行测试,验证了本文方法对未知智能语音算法的适用性。

Prediction and Analysis of Elevator Traffic Flow under the LSTM Neural Network

Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion within elevator systems. Many passengers experience dissatisfaction with prolonged wait times, leading to impatience and frustration among building occupants. The widespread adoption of neural networks and deep learning technologies across various fields and industries represents a significant paradigm shift, and unlocking new avenues for innovation and advancement. These cutting-edge technologies offer unprecedented opportunities to address complex challenges and optimize processes in diverse domains. In this study, LSTM (Long Short-Term Memory) network technology is leveraged to analyze elevator traffic flow within a typical office building. By harnessing the predictive capabilities of LSTM, the research aims to contribute to advancements in elevator group control design, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments. The findings of this research have the potential to reference the development of intelligent elevator management systems, capable of dynamically adapting to fluctuating passenger demand and optimizing elevator usage in real-time. By enhancing the efficiency and functionality of vertical transportation systems, the research contributes to creating more sustainable, accessible, and user-friendly living environments for individuals across diverse demographics.

Mining Social Groups with Weighted Similarity in Campus Wireless Network

With the popularity of wireless networks and the prevalence of personal mobile computing devices, understanding the characteristic of wireless network users is of great significance to the network performance. In this study, system logs from two universities, Dartmouth College and Shanghai Jiao Tong University(SJTU), were mined and analyzed. Every user's log was represented by a user profile. A novel weighted social similarity was proposed to quantify the resemblance of users considering influence of location visits. Based on the similarity, an unsupervised learning method was applied to cluster users. Though environment parameters are different, two universities both form many social groups with Pareto distribution of similarity and exponential distribution of group sizes. These findings are very important to the research of wireless network and social network .

Group2vec:基于无监督机器学习的基团向量表示及其物性预测应用

定量构效关系模型在化工产品设计中发挥着重要作用。基于自然语言处理技术的深度学习建模方法是构建定量构效关系模型的有效方法之一。提出一种基于基团词嵌入模型(Group2vec)的深度学习物性预测框架。首先,建立数据库用于预训练与物性预测。其次,利用基团分割方法,将数据库中分子SMILES文本转化为基团序列。再次,通过CBOW算法将基团序列进行词嵌入预训练,获得包含相似性结构信息的基团向量。最后,基于基团向量构建包含注意力机制的深度学习模型,并在不同物性数据库上进行模型测试,同时将其与现有模型进行比较,对比结果表明基于Group2vec的深度学习物性预测模型不仅具有较高的预测准确性与通用性,也具备一定的可解释性。

Reliable Content Distribution in P2P Networks Based on Peer Groups

Peer-to-Peer (P2P) networks are highly dynamic systems which are very popular for content distribution in the Internet. A single peer remains in the system for an unpredictable amount of time, and the rate in which peers enter and leave the system, i.e. the churn, is often high. A user that is obtaining content from a selected peer is frequently informed that particular peer is not available anymore, and is asked to select another peer, or will have another peer assigned, often without enough checks to confirm that the content provided by the new peer presents the same quality of the previous peer. In this work we present a strategy based on group communication for transparent and robust content access in P2P networks. Instead of accessing a single peer for obtaining the desired content, a user request is received and processed by a group of peers. This group of peers, called PCG (Peer Content Group) provides reliable content access in sense that even as members of the group crash or leave the system, users continue to receive the content if at least one group member remains fault-free. Each PCG member is capable of independently serving the request. A PCG is transparent to the user, as the group interface is identical to the interface provided by a single peer. A group member is elected to serve each request. A fault monitoring component allows the detection of member crashes. If the peer is serving request crashes, another group member is elected to continue providing the service. The PCG and a P2P file sharing applications were implemented in the JXTA platform. Evaluation results are presented showing the latency of group operations and system components.

A target group tracking algorithm based on a hybrid sensor network

Traditional tracking algorithms based on static sensors have several problems. First, the targets only occur in a part of the interested area; however, a large number of static sensors are distributed in the area to guarantee entire coverage, which leads to wastage of sensor resources. Second, many static sensors have to remain in active mode to track the targets, which causes an increase of energy consumption. To solve these problems, a target group tracking algorithm based on a hybrid sensor network is proposed in this paper, which includes static sensors and mobile sensors. First, an estimation algorithm is proposed to estimate the objective region by static sensors, which work in low-power sensing mode. Second, a movement algorithm based on sliding windows is proposed for mobile sensors to obtain the destinations. Simulation results show that this algorithm can reduce the number of mobile sensors participating in the tracking task and prolong the network lifetime.

A target group tracking algorithm for wireless sensor networks using azimuthal angle of arrival information

In this paper, we explore the technology of tracking a group of targets with correlated motions in a wireless sensor network. Since a group of targets moves collectively and is restricted within a limited region, it is not worth consuming scarce resources of sensors in computing the trajectory of each single target. Hence, in this paper, the problem is modeled as tracking a geographical continuous region covered by all targets. A tracking algorithm is proposed to estimate the region covered by the target group in each sampling period. Based on the locations of sensors and the azimuthal angle of arrival （AOA） information, the estimated region covering all the group members is obtained. Algorithm analysis provides the fundamental limits to the accuracy of localizing a target group. Simulation results show that the proposed algorithm is superior to the existing hull algorithm due to the reduction in estimation error, which is between 10% and 40% of the hull algorithm, with a similar density of sensors. And when the density of sensors increases, the localization accuracy of the proposed algorithm improves dramatically.

ZTE to Supply Bearer Network Solution to Finnet Group

ZTE Corporation, a leading global provider of telecommunications equipment and network solutions, announced an agreement with Westlink part of the Finish telecommunications group Finnet, to supply a unified multi-service metro bearer solution on February 18, 2010.

GENERALIZED INVERSE GROUP OF SIGNAL AND ITS IMPLEMENTATION WITH NEURAL NETWORKS

A new concept, the generalized inverse group (GIG) of signal, is firstly proposed and its properties, leaking coefficients and implementation with neural networks are presented. Theoretical analysis and computational simulation have shown that (1) there is a group of finite length of generalized inverse signals for any given finite signal, which forms the GIG; (2) each inverse group has different leaking coefficients, thus different abnormal states; (3) each GIG can be implemented by a grouped and improved single-layer perceptron which appears with fast convergence. When used in deconvolution, the proposed GIG can form a new parallel finite length of filtering deconvolution method. On off-line processing, the computational time is reduced to O(N) from O(N2). And the less the leaking coefficient is, the more reliable the deconvolution will be.

A Novel Approach towards Cost Effective Region-Based Group Key Agreement Protocol for Ad Hoc Networks Using Elliptic Curve Cryptography

This paper addresses an interesting security problem in wireless ad hoc networks: the dynamic group key agreement key establishment. For secure group communication in an ad hoc network, a group key shared by all group members is required. This group key should be updated when there are membership changes (when the new member joins or current member leaves) in the group. In this paper, we propose a novel, secure, scalable and efficient region-based group key agreement protocol for ad hoc networks. This is implemented by a two-level structure and a new scheme of group key update. The idea is to divide the group into subgroups, each maintaining its subgroup keys using group elliptic curve diffie-hellman (GECDH) Protocol and links with other subgroups in a tree structure using tree-based group elliptic curve diffie-hellman (TGECDH) protocol. By introducing region-based approach, messages and key updates will be limited within subgroup and outer group;hence computation load is distributed to many hosts. Both theoretical analysis and experimental results show that this Region-based key agreement protocol performs well for the key establishment problem in ad hoc network in terms of memory cost, computation cost and communication cost.

Cascading failure in multilayer networks with dynamic dependency groups＊

The cascading failure often occurs in real networks. It is significant to analyze the cascading failure in the complex network research. The dependency relation can change over time. Therefore, in this study, we investigate the cascading fail- ure in multilayer networks with dynamic dependency groups. We construct a model considering the recovery mechanism. In our model, two effects between layers are defined. Under Effect 1, the dependent nodes in other layers will be disabled as long as one node does not belong to the largest connected component in one layer. Under Effect 2, the dependent nodes in other layers will recover when one node belongs to the largest connected component. The theoretical solution of the largest component is deduced and the simulation results verify our theoretical solution. In the simulation, we analyze the influence factors of the network robustness, including the fraction of dependent nodes and the group size, in our model. It shows that increasing the fraction of dependent nodes and the group size will enhance the network robustness under Effect 1. On the contrary, these will reduce the network robustness under Effect 2. Meanwhile, we find that the tightness of the network connection will affect the robustness of networks. Furthermore, setting the average degree of network as 8 is enough to keep the network robust.

ZTE Partners with KPN Group Belgium to Deploy Packet-Switched Core Network

27 August 2012--ZTE Corporation has signed a deal on a packet-switched core network （CN） for KPN Group Belgium （KPNGB）. KPNGB will deploy ZTE＇s packet-switched CN equipment, which supports unified radio access. The contract is the second of its kind between ZTE and KPNfollows from a construction project with KPN Germany （E-Plus） that was completed in September 2010.

基于网络群组特征的生态管理分区--以武汉市为例

生态管理分区是维持区域生态安全、实现城市生态差异化治理的重要手段。然而现有分区方法侧重生态功能属性,较少考虑生态斑块之间联系强度差异,忽视了斑块的群组结构。以武汉市为例构建生态网络,从生态系统结构和功能的视角,结合斑块空间组织和斑块联系强弱,运用凝聚子群方法,提取联系紧密的生态组分,将网络划分为异质性群组,基于网络群组特征和生态景观辐射范围进行分区覆盖分析,并进行分区评价。结果表明:(1)86条生态廊道连接研究区34处生态斑块,进一步形成8个生态群组;(2)多数群组内部连通性较好,北部群组之间联系较强,南部群组之间联系相对较弱;(3)依据群组结构功能特征,形成6大网络群组分区,通过与武汉市经济发展规划分区对比,两者具有较高的一致性;(4)城市外围的分区网络稳定性较好,中部稳定性较差,识别分区内13个重要斑块和16条重要廊道,作为重点发展保护对象;(5)综合分区特征将6大分区确立为生态屏障区、生态控制区、生态改善区、生态修复区、生态开发区以及生态保育区,并提出生态发展差异化保护措施。研究将生态功能属性和空间结构属性进行有机关联并制定分区策略,为区域生态管理分区、生态保护规划提供新视角。

基于卷积神经网络的“舌边白涎”舌象识别研究

目的通过机器学习分析“舌边白涎”舌象特性,对舌象进行局部特征识别研究,探讨卷积神经网络算法在舌象识别应用中的性能。方法使用Python进行图像预处理,搭建用于舌象识别的视觉几何组16层(visual geometry group 16,VGG16)卷积神经网络模型,分析其对“舌边白涎”舌象鉴别分析的效果,并结合热力图分析“舌边白涎”典型舌象表现。结果基于PyTorch框架,进行卷积神经网络的舌象鉴别研究,VGG16及残差网络50层(residual network 50,ResNet50)模型验证准确率均较高,达到80%以上,且ResNet50模型优于VGG16模型,可为舌象识别提供一定参考。基于加权梯度类激活映射(gradient-weighted class activation mapping,Grad-CAM)技术,通过舌苔舌色差异分布的网络可视化,有助于直观进行模型评估分析。结论基于卷积神经网络模型对舌象数据库进行分析,实现“舌边白涎”舌象识别,有助于临床诊疗的客观化辅助分析,为舌诊智能化发展提供一定借鉴。

青年“emo时听红歌”:现象、本质与启示

当前网络空间中越来越多的青年采用听红歌的方式来抵抗“emo”情绪,并逐渐形成了“emo时听红歌”的青年亚文化现象。研究发现“emo时听红歌”现象呈现出了多平台交叉的传播轨迹、“星星之火”的传播样态和内容情感积极正向的话语表征;“emo时听红歌”现象的本质是当代青年的情绪自我疗愈,是其对红歌情感价值和精神价值的主动找寻,客观上兼具与消极音乐文化对抗的效果;“emo时听红歌”的功能机理主要源于“磁场论”下的行为自洽、情绪调节下的音乐疗愈和情感体验下的精神鼓舞;“emo时听红歌”圈群面临着组织聚合度不足、群体认同感不足和亚文化产出不足的发展困境。面对“emo时听红歌”现象,应当重视当代青年的情绪需求,不断增强青年对红歌的文化自信,推进红歌文化的守正创新,发挥红色主流音乐文化提升新时代青年精气神的重要功能和价值。

基于注意力改进残差网络结构的表情识别方法

针对目前CNN在复杂图像中特征提取不充分的问题,提出一种基于注意力的改进残差网络的表情识别网络。设计一个双流网络在完成粗特征表情识别的同时检测关键点,并使用注意力机制增大关键点周边特征的权重。随后以残差网络为基础模型,改进残差块之间的跳跃连接方式,并将残差块中的普通卷积改进为分组卷积来强化特征提取能力。最后联合两个表情识别网络进行分类,实验结果验证了该模型方案有着更卓越的性能。