Emoji Essence:Detecting User Emotional Response on Visual Centre Field with Emoticons

User experience is understood in so many ways,like a one on one interaction(subjective views),online surveys and questionnaires.This is simply so get the user’s implicit response,this paper demonstrates the underlying user emotion on a particular interface such as the webpage visual content based on the context of familiarisation to convey users’emotion on the interface using emoji,we integrated physiological readings and eye movement behaviour to convey user emotion on the visual centre field of a web interface.The physiological reading is synchronised with the eye tracker to obtain correlating user interaction,and emoticons are used as a form of emotion conveyance on the interface.The eye movement prediction is obtained through a control system’s loop and is represented by different color display of gaze points(GT)that detects a particular user’s emotion on the webpage interface.These are interpreted by the emoticons.Result shows synchronised readings which correlates to area of interests(AOI)of the webpage and user emotion.These are prototypical instances of authentic user response execution for a computer interface and to easily identify user response without user subjective response for better and easy design decisions.

Mobile Business Process Personalization for End Users

Many specified business needs in enterprise context cannot be effectively satisfied using current business process technology.This phenomenon is called the "long tail" of business processes.In addition,more and more business applications need to be accessed from mobile devices such as smartphones by enterprise end users.This paper attempts to solve both two challenges above.A lightweight event-driven process model is proposed aiming at satisfying the spontaneous business needs in enterprise.And we design an innovative wizard,which works like a tutorial,guiding end users in creating this lightweight process model.Moreover,end users are allowed to interact with the process created by themselves on smartphones.Finally,the usability of our approach was evaluated on a small set of users in a real business scenario.The results show that end users can effectively build their personalized business processes using our approach and interact with them in mobile environment.

Insider Attack Detection Using Deep Belief Neural Network in Cloud Computing

Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information quickly.The use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing efficiently.This cloud is nowadays highly affected by internal threats of the user.Sensitive applications such as banking,hospital,and business are more likely affected by real user threats.An intruder is presented as a user and set as a member of the network.After becoming an insider in the network,they will try to attack or steal sensitive data during information sharing or conversation.The major issue in today's technological development is identifying the insider threat in the cloud network.When data are lost,compromising cloud users is difficult.Privacy and security are not ensured,and then,the usage of the cloud is not trusted.Several solutions are available for the external security of the cloud network.However,insider or internal threats need to be addressed.In this research work,we focus on a solution for identifying an insider attack using the artificial intelligence technique.An insider attack is possible by using nodes of weak users’systems.They will log in using a weak user id,connect to a network,and pretend to be a trusted node.Then,they can easily attack and hack information as an insider,and identifying them is very difficult.These types of attacks need intelligent solutions.A machine learning approach is widely used for security issues.To date,the existing lags can classify the attackers accurately.This information hijacking process is very absurd,which motivates young researchers to provide a solution for internal threats.In our proposed work,we track the attackers using a user interaction behavior pattern and deep learning technique.The usage of mouse movements and clicks and keystrokes of the real user is stored in a database.The deep belief neural network is designed using a restricted Boltzmann machine(RBM)so that the layer of RBM communicates with the previous and subsequent layers.The result is evaluated using a Cooja simulator based on the cloud environment.The accuracy and F-measure are highly improved compared with when using the existing long short-term memory and support vector machine.

日式餐具设计中的符号互动论研究

目的:以日式餐具设计为例探讨符号互动论的作用与意义,分析日式餐具设计的内涵,为餐具设计提供新的设计原则与思路。方法基于符号互动论的核心观点分析日式餐具设计,捕捉现代人心理和行为的新特征,提出日式餐具设计中的符号互动论基本原则与设计方法。结论:在日式餐具设计中,符号互动论的核心观点能够帮助设计师综合考虑设计因素,在关注日式餐具设计的同时,强调餐具使用的情景体验与用户感受等综合因素,体现出设计师的反思。

Design and Realization of an NFC-Driven Smart Home System to Support Intruder Detection and Social Network Integration

The Internet of thing（Io T） emerges as a possible solution to realize a smart life in the modern age. In this article, we design and realize a novel near field communication（NFC）-driven smart home system for Io T, which integrates the wireless sensor network（WSN）, social networks, and the cloud computing. NFC technology provides a way for users to exchange information between them and the system by simply contacting. So, we propose to use NFC as the system drive in the architecture, such that users can intuitively interact with the system and deliver their intentions. Then, the corresponding service over the system will control or adjust the “things” at home to fit users＇ needs. Furthermore, the proposed system provides a platform for developers to easily and rapidly implement their smart home related services. In the system, WSN sensing and control, NFC communications and identification, user profile management and preference analysis, and social network integration are all provided as platform services. We will show how the system works for home automation, intruder detection, and social network sharing.

Metaverse:Perspectives from graphics,interactions and visualization

The metaverse is a visual world that blends the physical world and digital world.At present,the development of the metaverse is still in the early stage,and there lacks a framework for the visual construction and exploration of the metaverse.In this paper,we propose a framework that summarizes how graphics,interaction,and visualization techniques support the visual construction of the metaverse and user-centric exploration.We introduce three kinds of visual elements that compose the metaverse and the two graphical construction methods in a pipeline.We propose a taxonomy of interaction technologies based on interaction tasks,user actions,feedback and various sensory channels,and a taxonomy of visualization techniques that assist user awareness.Current potential applications and future opportunities are discussed in the context of visual construction and exploration of the metaverse.We hope this paper can provide a stepping stone for further research in the area of graphics,interaction and visualization in the metaverse.

Quality of experience evaluation of HTTP video streaming based on user interactive behaviors

User interactive behaviors play a dual role during the hypertext transfer protocol （HTTP） video service： reflection and influence. However, they are seldom taken into account in practices. To this end, this paper puts forward the user interactive behaviors, as subjective factors of quality of experience （QoE） from viewer level, to structure a comprehensive multilayer evaluation model based on classic network quality of service （QoS） and application QoS. First, dual roles of user behaviors are studied and the characteristics are extracted where the user experience is correlated with user interactive behaviors. Furthermore, we categorize QoE factors into three dimensions and build the metric system. Then we perform the subjective tests and investigate the relationships among network path quality, user behaviors, and QoE. Ultimately, we employ the back propagation neural network （BPNN） to validate our analysis and model. Through the simulation experiment of mathematical and BPNN, the dual effects of user interaction behaviors on the reflection and influence of QoE in the video stream are analyzed, and the QoE metric system and evaluation model are established.

Sequential interactive image segmentation

Interactive image segmentation(IIS)is an important technique for obtaining pixel-level annotations.In many cases,target objects share similar semantics.However,IIS methods neglect this connection and in particular the cues provided by representations of previously segmented objects,previous user interaction,and previous prediction masks,which can all provide suitable priors for the current annotation.In this paper,we formulate a sequential interactive image segmentation(SIIS)task for minimizing user interaction when segmenting sequences of related images,and we provide a practical approach to this task using two pertinent designs.The first is a novel interaction mode.When annotating a new sample,our method can automatically propose an initial click proposal based on previous annotation.This dramatically helps to reduce the interaction burden on the user.The second is an online optimization strategy,with the goal of providing semantic information when annotating specific targets,optimizing the model with dense supervision from previously labeled samples.Experiments demonstrate the effectiveness of regarding SIIS as a particular task,and our methods for addressing it.

A Survey of Visual Analytics Techniques and Applications: State-of-the-Art Research and Future Challenges

Visual analytics employs interactive visualizations to integrate users＇ knowledge and inference capability into numerical/algorithmic data analysis processes. It is an active research field that has applications in many sectors, such as security, finance, and business. The growing popularity of visual analytics in recent years creates the need for a broad survey that reviews and assesses the recent developments in the field. This report reviews and classifies recent work into a set of application categories including space and time, multivariate, text, graph and network, and other applications. More importantly, this report presents analytics space, inspired by design space, which relates each application category to the key steps in visual analytics, including visual mapping, model-based analysis, and user interactions. We explore and discuss the analytics space to acld the current understanding and better understand research trends in the field.

A survey of recent interactive image segmentation methods

Image segmentation is one of the most basic tasks in computer vision and remains an initial step of many applications. In this paper, we focus on interactive image segmentation(IIS), often referred to as foreground–background separation or object extraction, guided by user interaction. We provide an overview of the IIS literature by covering more than 150 publications, especially recent works that have not been surveyed before. Moreover, we try to give a comprehensive classification of them according to different viewpoints and present a general and concise comparison of the most recent published works. Furthermore, we survey widely used datasets,evaluation metrics, and available resources in the field of IIS.

Improving Shape from Shading with Interactive Tabu Search

Optimisation based shape from shading （SFS） is sensitive to initialization： errors in initialization are a signifi- cant cause of poor overall shape reconstruction. In this paper, we present a method to help overcome this problem by means of user interaction. There are two key elements in our method. Firstly, we extend SFS to consider a set of initializations, rather than to use a single one. Secondly, we efficiently explore this initialization space using a heuristic search method, tabu search, guided by user evaluation of the reconstruction quality. Reconstruction results on both synthetic and real images demonstrate the effectiveness of our method in providing more desirable shape reconstructions.

Anomaly Detection in Microblogging via Co-Clustering

Traditional anomaly detection on microblogging mostly focuses on individual anomalous users or messages. Since anomalous users employ advanced intelligent means, the anomaly detection is greatly poor in performance. In this paper, we propose an innovative framework of anomaly detection based on bipartite graph and co-clustering. A bipartite graph between users and messages is built to model the homogeneous and heterogeneous interactions. The proposed co- clustering algorithm based on nonnegative matrix tri-factorization can detect anomalous users and messages simultaneously. The homogeneous relations modeled by the bipartite graph are used as constraints to improve the accuracy of the co- clustering algorithm. Experimental results show that the proposed scheme can detect individual and group anomalies with high accuracy on a Sina Weibo dataset.

An Interactive platform for low-cost 3D building modeling from VGI data using convolutional neural network

The applications of 3D building models are limited as producing them requires massive labor and time costs as well as expensive devices.In this paper,we aim to propose a novel and web-based interactive platform,VGI3D,to overcome these challenges.The platform is designed to reconstruct 3D building models by using free images from internet users or volunteered geographic informa-tion(VGI)platform,even though not all these images are of high quality.Our interactive platform can effectively obtain each 3D building model from images in 30 seconds,with the help of user interaction module and convolutional neural network(CNN).The user interaction module provides the boundary of building facades for 3D building modeling.And this CNN can detect facade elements even though multiple architectural styles and complex scenes are within the images.Moreover,user interaction module is designed as simple as possible to make it easier to use for both of expert and non-expert users.Meanwhile,we conducted a usability testing and collected feedback from participants to better optimize platform and user experience.In general,the usage of VGI data reduces labor and device costs,and CNN simplifies the process of elements extraction in 3D building modeling.Hence,our proposed platform offers a promising solution to the 3D modeling community.

A Multimedia Synchronization Model Based on Timed Petri Net

Multimedia synchronization is a significant requirement for distributed multimedia applications. In this paper, a multimedia synchronization model based on timed Petri nets is presented. Using this model one can give the abstraction and formal description for multimedia object compositions with time-constrained relations. Algorithms for asynchronous user interactions are also presented.