A memory mechanism has attracted growing popularity in tracking tasks due to the ability of learning long-term-dependent information. However, it is very challenging for existing memory modules to provide the intrinsic attribute information of the target to the tracker in complex scenes. In this article, by considering the biological visual memory mechanisms, we propose the novel online tracking method via an attention-driven memory network, which can mine discriminative memory information and enhance the robustness and reliability of the tracker. First, to reinforce effectiveness of memory content, we design a novel attention-driven memory network. In the network, the long memory module gains property-level memory information by focusing on the state of the target at both the channel and spatial levels. Meanwhile, in reciprocity, we add a short-term memory module to maintain good adaptability when confronting drastic deformation of the target. The attention-driven memory network can adaptively adjust the contribution of short-term and long-term memories to tracking results under the weighted gradient harmonized loss. On this basis, to avoid model performance degradation, an online memory updater (MU) is further proposed. It is designed to mining for target information in tracking results through the Mixer layer and the online head network together. By evaluating the confidence of the tracking results, the memory updater can accurately judge the time of updating the model, which guarantees the effectiveness of online memory updates. Finally, the proposed method performs favorably and has been extensively validated on several benchmark datasets, including object tracking benchmark-50/100 (OTB-50/100), temple color-128 (TC-128), unmanned aerial vehicles-123 (UAV-123), generic object tracking -10k (GOT-10k), visual object tracking-2016 (VOT-2016), and VOT-2018 against several advanced methods.

The purpose of this study is to present a novel method which can objectively identify the subjective perception of tonic pain. To achieve this goal, scalp EEG data are recorded from 16 subjects under the cold stimuli condition. The proposed method is capable of classifying four classes of tonic pai n states, which include No pain, Minor Pain, Moderate Pain, and Severe Pain. Due to multi-class problem of our research an extended Common Spatial Pattern (ECSP) method is first proposed for accurately extracting features of tonic pain from captured EEG data. Then, a single-hidden-layer feedforward network is used as a classifier for pain identification. With the aid of extreme learning machine (ELM) algorithm, the classifier is trained here. The advantages of ELM-based classifier can obtain an optimal and generalized solution for multi-class tonic cold pain. Experimental results demonstrate that the proposed method discriminates the tonic pain successfully. Additionally, to show the superiority for the ELM-based classifier, compared results with the well-known support vector machine (SVM) method show the ELM-based classifier outperform than the SVM-based classifier. These findings may pay the way for providing a direct and objective measure of the subjective perception of tonic pain.

Eye detection plays an important role in many fields, because eyes provide prominent facial feature information. However, changes in face pose, illumination variation, with glasses, and eye occlusions can make it difficult to detect eyes well from facial images. This paper proposes a hybrid model f or eye detection. The model is an integration of two classifiers: Convolutional Neural Networks (CNN) and Support Vector Machines (SVM). In order to improve the speed of detection in the system, an eye variance filter (EVF) is constructed for eliminating most of noneye images to keep less candidate eye images. The CNN then works as a trainable feature extractor to explicitly extract various latent eye features. Finally, the trained SVM classifier is employed for eye verification instead of using the CNN classification function. Experiments applying the model have been conducted on the BioID, IMM, FERET and ORL face databases. Comparisons with other methods on the same databases indicate that this hybrid model has achieved a higher detection accuracy. Extensive experiments demonstrate the robustness and efficiency of our method by testing it on different facial images with varying eye conditions.

In group decision making, it is inevitable that the individual decision maker’s subjectivity is involved, which causes difficulty in reaching a group decision. One of the difficulties is to aggregate a small set of expert opinions with the individual subjectivity or uncertainty modeled with probability theory. This difficult problem is called probability distribution function aggregation (DFA). This paper presents a simple and efficient approach to the DFA problem. The main idea of the proposed approach is that the DFA problem is modeled as a nonlinear function of a set of probability distribution functions, and then a linear feedback iteration scheme is proposed to solve the nonlinear function, leading to a group judgment or decision. Illustration of this new approach is given by a well-known DFA example which was solved with the Delphi method. The DFA problem is a part of the group decision problem. Therefore, the proposed algorithm is also useful to the decision making problem in general. Another contribution of the this paper is the proposed notation of systematically representing the imprecise group decision problem with the classification of imprecise information into three classes, namely incomplete information, vague information, and uncertain information. The particular DFA problem dealt with in this paper is then characterized with this general notation.

Eye movements mainly consist of fixations and saccades. The identification of eye fixations plays an important role in the process of eye-movement data research. At present, there is no standard method for identifying eye fixations. In this paper, eye movements are regarded as spatial-temporal traj ectories. Hence, we present a spatial-temporal trajectory clustering algorithm for eye fixations identification. The main idea of the algorithm is based on Density-Based Spatial Clustering Algorithm with Noise (DBSCAN), which is commonly used in spatial clustering data. In order to apply DBSCAN to our spatial-temporal clustering data, we modified its original concept and algorithm. In addition, the optimum dispersion threshold (Eps) is derived automatically from the data sets with the aid of the `gap statistic' theory. Using the confusion matrix measurement method, we compared the classification results obtained by our algorithm with four other expert algorithms for eye fixations identification show the proposed algorithm demonstrated an equal or better performance. Also, the robustness of our algorithm to additional noise in Points of Gaze (PoGs) data and changes in sampling rate has been verified.

In this paper, we present a driver-automation system for providing brake assistance to the driver using the concepts of various levels of intervention. The system offers potential for further development and integration into main stream cars using by-wire technology, both from passenger safety and system usability perspectives. The criticality of the situation of a potential crash is estimated using a Bayesian framework from literature which considers not only the vehicle's situational need for braking but also the driver's intent to apply brakes. The novel idea of our work is a well-defined dictionary for critical zone segmentation is incorporated into the system to decide the appropriate intervention level by the automation system for each situation, and appropriate assistance is provided to the driver, in terms of not only communicating auditory messages to the driver but also applying automatic brakes if need be to shift the criticality from a high/very high to a low zone. The second idea of our work is the feedback control of the amount of automatic braking necessary to be applied by the system. Results from sample runs of simulated testing of the system have been reported and future scope of work towards improvement is discussed.

In human–machine collaboration, automated machines may assist operators in a variety of ways. However, chaotic assistance may lead to negative consequences, which makes the achievement of effective coordination of the different types of assistance all the more important. This paper discusses the classification of assistance on a cognitive basis and a method of coordinating assistance. Cognitive assistance is viewed as a 2-D problem, consisting of when to provide assistance (a control problem) and what assistance to provide (an interface problem). This paper further proposes dynamically controlling cognitive engagement levels to meet the demands of maintaining performance. Cognitive engagement control determines the appropriate moment to provide the proper level of cognitive assistance. To validate the above approach, a driving assistance experiment was conducted on a driving simulator. In the experiment, an intelligent assistance system monitored the real-time driving performance of human drivers, e.g., time headway and lateral deviation. Because of the importance of visual attention in driving performance, the system monitored the cognitive engagement status of drivers by measuring their eye movements with an eye tracker. Through five sessions of car-following driving tests, the coordinated cognitive assistance (named adaptive assistance) was compared with four other types of cognitive assistance: no aid, soft aid , soft intervention, and hard intervention. The experimental results confirmed that coordinated cognitive assistance is the most effective approach to provide assistance in both primary and secondary tasks. It also appears to be more enjoyable and less intrusive when compared with other individual types of cognitive assistance.

A recurrent neural network is proposed for solving the non-smooth convex optimization problem with the convex inequality and linear equality constraints. Since the objective function and inequality constraints may not be smooth, the Clarke's generalized gradients of the objective function and inequality constraints are employed to describe the dynamics of the proposed neural network. It is proved that the equilibrium point set of the proposed neural network is equivalent to the optimal solution of the original optimization problem by using the Lagrangian saddle-point theorem. Under weak conditions, the proposed neural network is proved to be stable, and the state of the neural network is convergent to one of its equilibrium points. Compared with the existing neural network models for non-smooth optimization problems, the proposed neural network can deal with a larger class of constraints and is not based on the penalty method. Finally, the proposed neural network is used to solve the identification problem of genetic regulatory networks, which can be transformed into a non-smooth convex optimization problem. The simulation results show the satisfactory identification accuracy, which demonstrates the effectiveness and efficiency of the proposed approach.

A neural-network-based adaptive approach is proposed for the leader-following control of multiagent systems. The neural network is used to approximate the agent's uncertain dynamics, and the approximation error and external disturbances are counteracted by employing the robust signal. When there is no control input constraint, it can be proved that all the following agents can track the leader's time-varying state with the tracking error as small as desired. Compared with the related work in the literature, the uncertainty in the agent's dynamics is taken into account; the leader's state could be time-varying; and the proposed algorithm for each following agent is only dependent on the information of its neighbor agents. Finally, the satisfactory performance of the proposed method is illustrated by simulation examples.

Intelligent transport systems (ITSs) usually include three principal elements: vehicle, driver, and road (or, more generally, the environment). The use of ITS data bus (IDB) has been proposed to build sharable standard interfaces for in-vehicle information systems. Despite broadband communication technologies, such as dedicated short-range communication (DSRC), which have been developed to provide high-quality roadside-vehicle communication services for intelligent highways, the existing IDB model has not paid enough attention to the demands of information exchanges between the roadside and onboard units. In this paper, a new model of the roadside IDB (RIDB) is proposed to improve the existing IDB architecture. The physical layer, data-link layer, and application layer of the new model are also discussed. A prototype system of the RIDB, which is based on the wireless 802.11b protocol, has been developed in a test site. The experimental results demonstrated that the RIDB is feasible to provide high-quality roadside-vehicle and roadside-roadside communication services. Other potential applications of the IDB, such as probe cars and intersection collision prevention, are also discussed. The RIDB proposed in this paper is potentially useful for the construction of an intelligent transport infrastructure.

Journal Article On integration of interface design methods: Can debates be resolved? Get access Y. Lin, Y. Lin * a Department of Mechanical and Industrial Engineering, Northeastern University, 360 Huntington Avenue, Boston, MA 02115, USA * Corresponding author. Tel.: +1 617 3738610; fax: +1 617 3732921. E-mail address: [email protected] (Y. Lin). Search for other works by this author on: Oxford Academic Google Scholar W.J. Zhang, W.J. Zhang * b Advanced Engineering Design Laboratory, Department of Mechanical Engineering, The University of Saskatchewan, Saskatoon, Sask., Canada S7N 5A9 * Corresponding author. Tel.: +1 617 3738610; fax: +1 617 3732921. E-mail address: [email protected] (Y. Lin). Search for other works by this author on: Oxford Academic Google Scholar R.J. Koubek, R.J. Koubek c The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, 310 Leonhard Building, University Park, PA 16802, USA Search for other works by this author on: Oxford Academic Google Scholar Ronald R. Mourant Ronald R. Mourant a Department of Mechanical and Industrial Engineering, Northeastern University, 360 Huntington Avenue, Boston, MA 02115, USA Search for other works by this author on: Oxford Academic Google Scholar Interacting with Computers, Volume 18, Issue 4, July 2006, Pages 709–722, https://doi.org/10.1016/j.intcom.2005.11.008 Published: 20 December 2005 Article history Received: 03 November 2004 Revision received: 01 November 2005 Accepted: 01 November 2005 Published: 20 December 2005

Human attention can only be inferred from certain causal clues. Such an inference process is of high uncertainty. Bayesian network (BN) is often used for modeling such a process; specifically different features that represent human attention can be fused to reach a consistent conclusion. Previous studies on BN have little consideration of so-called contextual features. In this paper, we propose a few contextual features related to human attention. A novel BN model is then formulated which combines both the contextual features and their corresponding observable behavioral features. At the end, an example is used to illustrate the potential use of the new BN model for human-machine interface design.

By using the fusion of contextual, visual and non-visual features, a model based on Dempster-Shafer (D-S) evidence theory is proposed to obtain a reliable driver's fatigue recognition. Firstly, an overall model structure is set up with respect to the selected features and key symptoms of driver's fatigue. Secondly, a set of heuristic knowledge rules are used to determine the basic probability assignment; and a modified evidence combination is adopted to combine multiple pieces of evidence including consistent and conflicting ones. Thirdly, decision policy based on the basic probability assignment is applied to fatigue recognition. At last, an example is given to illustrate the proposed fatigue recognition model.

In general, evaluation of human–machine interface design remains a challenging task. Specifically, there remains a lack of method for tracking effective human operator's attention. This paper presents a study aimed at devising such a method. This method is based on a combination of operators' eye movement and hand movement behaviors. The eye movement reflects the operators' cognitive process and attention allocation, while the hand movement reflects the operators' physical action, which is the result of a cognitive process. Effectiveness of that piece of cognition (eye movement) can therefore be evaluated based on the result of an action (hand movement). The said measure, which may be called the hand–eye measure, is examined for its sensitivity to a good or poor operation behavior and patterns that are further correlated to the operator's behavior and performance. At present, the patterns across the whole operation period are explored. A reference system is employed to validate the hand–eye measure.

Evaluation of human-machine interface design remains a challenging task. This paper presents a study on a more objective evaluation technique based on the combination of operators' eye movement and hand movement behaviors. The eye movement reflects the operators' cognitive process and attention allocation, and the hand movement reflects the operators' physical hand action. Combination of these two kinds of behaviors may provide an important measuring technique to evaluate different human-machine interfaces and may further give more detailed information about the uses of the information displayed. The experiment was conducted to develop this new technique. The result of the study is promising.