In case of dangerous driving, the in-vehicle robot can provide multimodal warnings to help the driver correct the wrong operation, so the impact of the warning signal itself on driving safety needs to be reduced. This study investigates the design of multimodal warnings for in-vehicle robots under driving safety warning scenarios. Based on transparency theory, this study addressed the content and timing of visual and auditory modality warning outputs and discussed the effects of different robot speech and facial expressions on driving safety. Two rounds of experiments were conducted on a driving simulator to collect vehicle data, subjective data, and behavioral data. The results showed that driving safety and workload were optimal when the robot was designed to use negative expressions for the visual modality during the comprehension (SAT 2) phase and speech at a rate of 345 words/minute for the auditory modality during the comprehension (SAT 2) and prediction (SAT 3) phases. The design guideline obtained from the study provides a reference for the interaction design of driver assistance systems with robots as the interface.

Based on the transparency theory, this study investigates the appropriate amount of transparency information expressed by the in-vehicle robot under two channels of voice and visual in a proactive interaction scenario. The experiments are to test and evaluate different transparency levels and combinations of information in different channels of the in-vehicle robot, based on a driving simulator to collect subjective and objective data, which focuses on users' safety, usability, trust, and emotion dimensions under driving conditions. The results show that appropriate transparency expression is able to improve drivers' driving control and subjective evaluation and that drivers need a different amount of transparency information in different types of tasks.

Human-robot co-driving is a development direction for driving assistance, in which a key scenario is that robot giving warnings of unsafe driving to the driver (or bad driving behavior correction). When there are safety dangers in human driving, the robot can warn and prompt the driver using its voice and expressions, among other things. Cognitive multichannel research based on SAT theory can divide the robot's active cues into three stages: perception, understanding, and prediction. There are different transparencies in the visual and auditory channels. This paper argues that a reasonable multimodal warning design for robots can lead to safer and better driving performance for drivers in this scenario. We tried to design multimodal warnings for robots based on SAT theory from when, what, and how. The visual and auditory channels of human-robot interaction are also explored in depth to optimize the facial expressions and speech of the robot. Two rounds of comparison experiments were completed in this paper, the first comparing three potentially optimal transparency schemes and the second comparing four cueing speech rates and two valences of facial expressions. Driving data and questionnaires were evaluated and the results showed that the driving safety and workload were optimal when the robot was designed to use negative expressions for the visual channel during the comprehension (L2) phase and speech at a rate of 345 words/minute for the auditory channel during the comprehension (L2) and prediction (L3) phases.

With the continuous development of intelligent product interaction technology, the facial expression design of virtual images on the interactive interface of intelligent products has become an important research topic. Based on the current research on facial expression design of existing intelligent products, we symmetrically mapped the PAD (pleasure–arousal–dominance) emotion value to the image design, explored the characteristics of abstract expressions and the principles of expression design, and evaluated them experimentally. In this study, the experiment of PAD scores was conducted on the emotion expression design of abstract expressions, and the data results were analyzed to iterate the expression design. The experimental results show that PAD values can effectively guide designers in expression design. Meanwhile, the efficiency and recognition accuracy of human communication with abstract expression design can be improved by facial auxiliary elements and eyebrows.

Anthropomorphic robots need to maintain effective and emotive communication with humans as automotive agents to establish and maintain effective human–robot performances and positive human experiences. Previous research has shown that the characteristics of robot communication positively affect human–robot interaction outcomes such as usability, trust, workload, and performance. In this study, we investigated the characteristics of transparency and anthropomorphism in robotic dual-channel communication, encompassing the voice channel (low or high, increasing the amount of information provided by textual information) and the visual channel (low or high, increasing the amount of information provided by expressive information). The results showed the benefits and limitations of increasing the transparency and anthropomorphism, demonstrating the significance of the careful implementation of transparency methods. The limitations and future directions are discussed.

Recently, researchers become increasingly interested in studying cooperation in evolutionary game theory by learning people's behavior patterns in real world. One of the patterns is that when some people contribute more to a group, their behaviors are more likely to be imitated by their neighbors. Inspired by this, we introduce a preferential selection mechanism that players have higher probability in learning from their contributed counterparts. We follow the lattice arrangement of players and conduct spatial prisoner's dilemma game. We define the contribution of individual by the payoffs of his four neighbors, and a larger value corresponds to greater contribution. In strategy updating stage, we first calculate the contribution of all players, and then, for each player, we propose one of his neighbors to imitate according to their contribution. At last, we decide to imitate or not by Femi's dynamics. The simulations have justified our acclaim that imitating contributed players can prompt cooperation. This paper helps us understand why and how cooperation emerges in real world.

Network coding opportunistic routing (NCOR) offers a promising solution for efficient data transmission in delay tolerant networks. Due to the multi-copy strategy and network coding nature, NCOR inevitably brings about a large number of heterogeneous copies, leading to over-consumption of limited network resources. To alleviate this situation, it is imperative to study how to reduce redundant copies in the network. In this paper, we propose a pre-decoding recovery mechanism (PDRM) that removes residual copies after the destination node obtains the original packet information. The PDRM consists of three operations: generating pre-decoding elements, maintaining immune-lists, and deleting redundant copies. In particular, the destination node generates a pre-decoding element, and then, sends it to other nodes in the network via an immune-list to help remove the residual copies. Here, the pre-decoding element is an acknowledgement indicating that the destination node has the necessary information to decode the corresponding original packet. As the core of the PDRM, the first operation enables the destination node to generate a pre-decoding element for each innovative packet without waiting for decoding the generation. Simulation results demonstrate that the PDRM achieves excellent results in improving network performance, and outperforms the existing recovery mechanism.

Mobile Crowd Sensing (MCS) is widely used in large-scale complex social sensing tasks even though it cannot offer reliable sensing quality yet due to the mobility restrictions. In order to solve the inadequate sensing opportunities provided by an MCS system, we focus on building a Hybrid Crowd Sensing (HCS) network by organizing both static and uncontrolled mobile nodes. We use the static node central opportunistic coverage to measure the sensing quality of HCS by analyzing the different features of coverage in the three regions and forming definitions. Our proposed approach can enable the static nodes to be deployed in the traditional hexagonal lattice, and mobile nodes to be located by smaller hexagonal lattices. Moreover, the analysis results demonstrate that the hexagonal lattice is more economical in both the number of mobile nodes needed by the seamless coverage SSA and network connectivity with the square grid. Finally, we make further analysis of the stream successful transmission probability, and find out that there are many complex influence factors of the static node central opportunistic coverage, such as the size of the time window T, the relative position of the start location and the end location, the number of mobile nodes participates stream transmission, mobile strategy of mobile nodes, opportunistic delegation mechanism, opportunistic routing mechanism, and so on. We modelize the lower limitation of it by a discrete Markov chain, and the simulation results show both the feasibility and rationality of using the static node central opportunistic coverage as the sensing quality metric.

As mobile crowd sensing (MCS) cannot provide reliable services, the quality of service (QoS) is a major research interest. Since the service node makes a decisive impact on two vital factors of QoS, service node selection is becoming a novel research direction in MCS. In this paper, we analyze the factors that need attention when selecting proper service nodes in MCS and define the service node selection problem (SNSP) as follows: finding the optimal set of service nodes, provided that optimizes multiple metrics of QoS simultaneously and satisfies the network resource constraint. Accordingly, we formulate a multiobjective optimization model (MOOM), which converts SNSP to a multiobjective optimization problem (MOOP). Since the MOOM considers the comprehensive effect of all service nodes on one metric as one objective of MOOP, it can handle the diversity of metrics and conflicts between them; in particular, it can flexibly change the metric system of QoS depending on different demands. To demonstrate the value and effectiveness of the proposed MOOM, we propose a paradigm of it and design a corresponding multiobjective optimization selection mechanism. This paradigm focuses on the influence of node spatiotemporal mobility on both data collection and data transmission. Extensive experiments and comparison on a real-world data show that MOOM is an effective model for selecting service nodes with both good coverage and transmission performances.

Mobile Crowd Sensing (MCS) is widely used in numerous large-scale complex social sensing tasks, which cannot offer reliable sensing quality due to the human mobility. In order to solve the inadequate sensing opportunities provided solely by a MCS system, we organize both static and uncontrolled mobile nodes to build a Hybrid Crowd Sensing (HCS) network. In order to find out a suitable coverage deployment strategy for HCS, we describe the sensing coverage process, and notice which always led by a special static node. Inspired by the central place theory, we propose static node center hexagonal deployment. By using this deployment method, static nodes are deploying in traditional hexagonal lattice. Sensing Service Area (SSA) is divided into regular hexagonal lattice seamless, which can locate mobile nodes. We analyze the hexagonal lattice partition and make comparisons with the square grid partition. The results demonstrate that it is more economical from both the minimum number of mobile nodes needed for seamless coverage SSA and network connectivity.

This article describes how to judge whether there are any faces in the video or image,if there are,it will count out the number of the faces.The principle of implementation is based on AdaBoost algorithm.This paper selectes Haar-like characteristics and trained cascaded classifiers to recognize the faces.The improvment is adjusting weight to every cascaded classifier dynamically,set heavy weight for cascaded classifiers with higher accuracy and low weight for cascaded classifiers with lower accuracy.Experimental results indicate that the method is fast and reliable and meets the requirement of real-time system.

Anomaly detection,which is one of intrusion detection,is playing a more and more important role in network security field.It describes the characteristics of normal behavior in the network,and then achieves intrusion detection by the way of the comparison of the deviation to the normal value.Association Rule is a typical method for data mining,which can describe the relationship of some strength of things under certain conditions.A system model is constructed for an efficient anomaly detection based on the association rule mining which for approaching to describe network character,according to the data which has been received from the networks.The model obtains the satisfactory results.

Image stitching is normally used to make up a seamless and high resolution with a set of the overlap parts of images and videos.It is one of important technologies for image processing.Presented the main step of the image mosaics,basic principle and advantages and disadvantages of the ration matching algorithm,based on the ratio matching algorithm,an improved algorithm of image stitching is presented in order to resolve the pseudo matching.Using the theory of geometric proportion,comparing with traditional methods,the algorithm can find the optimal position more quickly and more exactly.The experiments show that this method can eliminate false matches validly.

With the growing number of service techniques in data integration system,it is necessary to compose existing services dynamically according to service request.The service similarity was measured by the service ontology similarity based on service ontology.An optimized graph for service composition was constructed based on service similarity,therefore the service composition problem changed to tree search problem while the graph was changed to tree.And then an efficient algorithm based on the search tree was presented to accomplish service composition.The simulation proves,compared with existing methods,the method can ensure quality and efficiency while composing services automatically according to service request.

As H.264/AVC digital video becomes more prevalent, issues of copyright protection and authentication that appropriate for this standard become very important. In this paper, an authentication watermarking algorithm for H.264/AVC compressed video by modulating CAVLC code words of 4 × 4 luminance blocks is proposed. During the embedding process, the eligible code words are first identified, and then the modulating rules between these code words and the watermark bits are established. The watermark information can be extracted directly from the encoded stream without resorting to the original video, and merely requires decoding the CAVLC code from bit stream rather than decoding the whole video. Experimental results show that the proposed watermarking scheme can effectively embed watermark information with little bit rate increase and almost no quality degradation.

The paper presented an algorithm which fills the query interface by using machine learning based on the ana-lysis of mechanism of Deep Web query.The algorithm is able to extract data automatically.Firstly,a 2D table is constructed.The columns of the table are controllers extracted from pages of the Deep Web query interface.Then values of the table are filled by giving values to all the controllers.Next,a learning of classification is going to be achieved accor-ding to the result whether the extraction of data successfully or not.Finally,the data is extracted by constructing request string automatically through the results of the learning.The experiment shows that the algorithm runs effectively.

Image annotation,a technique for connecting image semantics and visual features,can be used to present image semantics well.A method for image annotation using relevance feedback log and semantic network is presented.Firstly,the image semantics are acquired by users'relevance feedback log;then semantic clustering is carried out based on semantic similarity;lastly automatic image annotation is realized through semantic propagation.The experimental results indicate that increasing images will be annotated as increasing relevance feedback log and the annotation accuracy will be stable with the increment of relevance feedback.

In Chinese the SVO (subject-verb-object) construction is often appears in the query in information retrieval. The SVO construction will be cut into several separate key words, if the traditional retrieval algorithm is used, it will degenerate to a normal Boolean search, because the semantic meaning may be different when the order of the key words differs, thus the semantic implied in the SVO construction will not be exists, that is to say result of an text-retrieval algorithm will reflected by the order of the key words. For this question: taking the advantages of the structural features of forward and inverted index, the paper gives the definition of query step, document step, analyses their reflection on the retrieval, and proposed the pre-processing algorithm to form the new retrieval model based on VSM. Subsequently, The ontology and semantic web support was given, the procedure of translating user query into formal query for semantic search was also given in detail.

提出了一种基于小波零树结构的图像盲水印算法,算法在嵌入水印时通过JND门限控制水印嵌入强度,在保持水印不可见的同时提高了水印嵌入量,实验证明该算法具有良好的鲁棒性。

Service-oriented architecture (SOA) is a component model. It connects the different functions of the application modules (called services) through well-defined interfaces and contracts between these services. In this paper, based on the clear research meaning of SOA, first of all, the relevance of the concept of SOA is proposed, then followed by focus on SOA-based computer simulation system design and realization, which uses the thought of service-oriented architecture and realizes the system's base class, the software features between modules, as well as cascade between modules to form a complete computer simulation software systems.