Query: Emotion recognition using Speech Processing Using k-nearest neighbor algorithm

Relevant passages:
Toward detecting emotions in spoken dialogs <=SEP=> The importance of automatically recognizing emotions from human speech has grown with the increasing role of spoken language interfaces in human-computer interaction applications. This paper explores the detection of domain-specific emotions using language and discourse information in conjunction with acoustic correlates of emotion in speech signals. The specific focus is on a case study of detecting negative and non-negative emotions using spoken language data obtained from a call center application. Most previous studies in emotion recognition have used only the acoustic information contained in speech. In this paper, a combination of three sources of information-acoustic, lexical, and discourse-is used for emotion recognition. To capture emotion information at the language level, an information-theoretic notion of emotional salience is introduced. Optimization of the acoustic correlates of emotion with respect to classification error was accomplished by investigating different feature sets obtained from feature selection, followed by principal component analysis. Experimental results on our call center data show that the best results are obtained when acoustic and language information are combined. Results show that combining all the information, rather than using only acoustic information, improves emotion classification by 40.7% for males and 36.4% for females (linear discriminant classifier used for acoustic information).

A database of German emotional speech <=SEP=> The article describes a database of emotional speech. Ten actors (5 female and 5 male) simulated the emotions, producing 10 German utterances (5 short and 5 longer sentences) which could be used in everyday communication and are interpretable in all applied emotions. The recordings were taken in an anechoic chamber with high-quality recording equipment. In addition to the sound electro-glottograms were recorded. The speech material comprises about 800 sentences (seven emotions * ten actors * ten sentences + some second versions). The complete database was evaluated in a perception test regarding the recognisability of emotions and their naturalness. Utterances recognised better than 80% and judged as natural by more than 60% of the listeners were phonetically labelled in a narrow transcription with special markers for voice-quality, phonatory and articulatory settings and articulatory features. The database can be accessed by the public via the internet (http://www.expressive-speech.net/emodb/).

Speech emotion recognition using hidden Markov models <=SEP=> In emotion classification of speech signals, the popular features employed are statistics of fundamental frequency, energy contour, duration of silence and voice quality. However, the performance of systems employing these features degrades substantially when more than two categories of emotion are to be classified. In this paper, a text independent method of emotion classification of speech is proposed. The proposed method makes use of short time log frequency power coefficients (LFPC) to represent the speech signals and a discrete hidden Markov model (HMM) as the classifier. The emotions are classified into six categories. The category labels used are, the archetypal emotions of Anger, Disgust, Fear, Joy, Sadness and Surprise. Adatabase consisting of 60 emotional utterances, each from twelve speakers is constructed and used to train and test the proposed system. Performance of the LFPC feature parameters is compared with that of the linear prediction Cepstral coefficients (LPCC) and mel-frequency Cepstral coefficients (MFCC) feature parameters commonly used in speech recognition systems. Results show that the proposed system yields an average accuracy of 78% and the best accuracy of 96% in the classification of six emotions. This is beyond the 17% chances by a random hit for a sample set of 6 categories. Results also reveal that LFPC is a better choice as feature parameters for emotion classification than the traditional feature parameters. 2003 Elsevier B.V. All rights reserved.

Emotion Recognition in Speech Using Neural Networks <=SEP=> Emotion recognition in speech is a topic on which little research has been done to-date. In this paper, we discuss why emotion recognition in speech is a significant and applicable research topic, and present a system for emotion recognition using oneclass-in-one neural networks. By using a large database of phoneme balanced words, our system is speakerand context-independent. We achieve a recognition rate of approximately 50% when testing eight emotions.

Nonlinear dimension reduction for EEG-based epileptic seizure detection <=SEP=> Approximately 0.1 percent of epileptic patients die from unexpected deaths. In general, for intractable seizures, it is crucial to have an algorithm to accurately and automatically detect the seizures and notify care-givers to assist patients. EEG signals are known as definitive diagnosis of seizure events. In this work, we utilize the frequency domain features (normalized in-band power spectral density) for the EEG channels. We applied a nonlinear data-embedding technique based on stochastic neighbor distance metric to capture the relationships among data elements in high dimension and improve the accuracy of seizure detection. This proposed data embedding technique not only makes it possible to visualize data in two or three dimensions, but also tackles the inherent difficulties regarding high dimensional data classification such as time complexity and memory requirement. We also applied a patient specific KNN classification to detect seizure and non-seizure events. The results indicate that our nonlinear technique provides significantly better visualization and classification efficiency (F-measure greater than 87%) compared to conventional dimension reduction approaches.

SVD-based collaborative filtering with privacy <=SEP=> Collaborative filtering (CF) techniques are becoming increasingly popular with the evolution of the Internet. Such techniques recommend products to customers using similar users' preference data. The performance of CF systems degrades with increasing number of customers and products. To reduce the dimensionality of filtering databases and to improve the performance, Singular Value Decomposition (SVD) is applied for CF. Although filtering systems are widely used by E-commerce sites, they fail to protect users' privacy. Since many users might decide to give false information because of privacy concerns, collecting high quality data from customers is not an easy task. CF systems using these data might produce inaccurate recommendations. In this paper, we discuss SVD-based CF with privacy. To protect users' privacy while still providing recommendations with decent accuracy, we propose a randomized perturbation-based scheme.

Hierarchical interpretations for neural network predictions <=SEP=> Deep neural networks (DNNs) have achieved impressive predictive performance due to their ability to learn complex, non-linear relationships between variables. However, the inability to effectively visualize these relationships has led to DNNs being characterized as black boxes and consequently limited their applications. To ameliorate this problem, we introduce the use of hierarchical interpretations to explain DNN predictions through our proposed method, agglomerative contextual decomposition (ACD). Given a prediction from a trained DNN, ACD produces a hierarchical clustering of the input features, along with the contribution of each cluster to the final prediction. This hierarchy is optimized to identify clusters of features that the DNN learned are predictive. Using examples from Stanford Sentiment Treebank and ImageNet, we show that ACD is effective at diagnosing incorrect predictions and identifying dataset bias. Through human experiments, we demonstrate that ACD enables users both to identify the more accurate of two DNNs and to better trust a DNN’s outputs. We also find that ACD’s hierarchy is largely robust to adversarial perturbations, implying that it captures fundamental aspects of the input and ignores spurious noise.

Moving Target Indication via RADARSAT-2 Multichannel Synthetic Aperture Radar Processing <=SEP=> With the recent launches of the German TerraSAR-X and the Canadian RADARSAT-2, both equipped with phased array antennas and multiple receiver channels, synthetic aperture radar, ground moving target indication (SAR-GMTI) data are now routinely being acquired from space. Defence R&D Canada has been conducting SAR-GMTI trials to assess the performance and limitations of the RADARSAT-2 GMTI system. Several SAR-GMTI modes developed for RADARSAT-2 are described and preliminary test results of these modes are presented. Detailed equations of motion of a moving target for multiaperture spaceborne SAR geometry are derived and a moving target parameter estimation algorithm developed for RADARSAT-2 (called the Fractrum Estimator) is presented. Limitations of the simple dual-aperture SAR-GMTI mode are analysed as a function of the signal-to-noise ratio and target speed. Recently acquired RADARSAT-2 GMTI data are used to demonstrate the capability of different system modes and to validate the signal model and the algorithm.

Personalized Tour Recommendation Based on User Interests and Points of Interest Visit Durations <=SEP=> Tour recommendation and itinerary planning are challenging tasks for tourists, due to their need to select Points of Interest (POI) to visit in unfamiliar cities, and to select POIs that align with their interest preferences and trip constraints. We propose an algorithm called PERSTOUR for recommending personalized tours using POI popularity and user interest preferences, which are automatically derived from real-life travel sequences based on geotagged photos. Our tour recommendation problem is modelled using a formulation of the Orienteering problem, and considers user trip constraints such as time limits and the need to start and end at specific POIs. In our work, we also reflect levels of user interest based on visit durations, and demonstrate how POI visit duration can be personalized using this time-based user interest. Using a Flickr dataset of four cities, our experiments show the effectiveness of PERSTOUR against various baselines, in terms of tour popularity, interest, recall, precision and F1-score. In particular, our results show the merits of using time-based user interest and personalized POI visit durations, compared to the current practice of using frequency-based user interest and average visit durations.

MaJIC: Compiling MATLAB for Speed and Responsiveness <=SEP=> This paper presents and evaluates techniques to improve the execution performance of MATLAB. Previous efforts concentrated on source to source translation and batch compilation; MaJIC provides an interactive frontend that looks like MATLAB and compiles/optimizes code behind the scenes in real time, employing a combination of just-in-time and speculative ahead-of-time compilation. Performance results show that the proper mixture of these two techniques can yield near-zero response time as well as performance gains previously achieved only by batch compilers.

NEAT-o-Games: blending physical activity and fun in the daily routine <=SEP=> This article describes research that aims to encourage physical activity through a novel pervasive gaming paradigm. Data from a wearable accelerometer are logged wirelessly to a cell phone and control the animation of an avatar that represents the player in a virtual race game with other players over the cellular network. Winners are declared every day and players with an excess of activity points can spend some to get hints in mental games of the suite, like Sudoku. The racing game runs in the background throughout the day and every little move counts. As the gaming platform is embedded in the daily routine of players, it may act as a strong behavioral modifier and increase everyday physical activity other than volitional sporting exercise. Such physical activity (e.g., taking the stairs), is termed NEAT and was shown to play a major role in obesity prevention and intervention. A pilot experiment demonstrates that players are engaged in NEAT-o-Games and become more physically active while having a good dosage of fun.

A novel 8T SRAM with minimized power and delay <=SEP=> In this paper, a novel 8T SRAM cell is proposed which aims at decreasing the delay and lowering the total power consumption of the cell. The threshold voltage variations in the transistor affect the read and write stability of the cell. Also, power dissipation increases with the number of transistors which in turn affects the read and write stability. The proposed 8T SRAM bitcell is designed using 180 nm CMOS, n-well technology with a supply voltage of 1.8 V. The results show that the average delay has been improved by 80 % compared to the conventional 6T cell. The total power is improved by 14.5 % as compared to conventional 6T SRAM cell.

Texture analysis of medical images. <=SEP=> The analysis of texture parameters is a useful way of increasing the information obtainable from medical images. It is an ongoing field of research, with applications ranging from the segmentation of specific anatomical structures and the detection of lesions, to differentiation between pathological and healthy tissue in different organs. Texture analysis uses radiological images obtained in routine diagnostic practice, but involves an ensemble of mathematical computations performed with the data contained within the images. In this article we clarify the principles of texture analysis and give examples of its applications, reviewing studies of the technique.

f3.js: A Parametric Design Tool for Physical Computing Devices for Both Interaction Designers and End-users <=SEP=> Although the exploration of design alternatives is crucial for interaction designers and customization is required for end-users, the current development tools for physical computing devices have focused on single versions of an artifact. We propose the parametric design of devices including their enclosure layouts and programs to address this issue. A Web-based design tool called f3.js is presented as an example implementation, which allows devices assembled from laser-cut panels with sensors and actuator modules to be parametrically created and customized. It enables interaction designers to write code with dedicated APIs, declare parameters, and interactively tune them to produce the enclosure layouts and programs. It also provides a separate user interface for end-users that allows parameter tuning and dynamically generates instructions for device assembly. The parametric design approach and the tool were evaluated through two user studies with interaction designers, university students, and end-users.

Recurrent Transformer Networks for Semantic Correspondence <=SEP=> We present recurrent transformer networks (RTNs) for obtaining dense correspondences between semantically similar images. Our networks accomplish this through an iterative process of estimating spatial transformations between the input images and using these transformations to generate aligned convolutional activations. By directly estimating the transformations between an image pair, rather than employing spatial transformer networks to independently normalize each individual image, we show that greater accuracy can be achieved. This process is conducted in a recursive manner to refine both the transformation estimates and the feature representations. In addition, a technique is presented for weakly-supervised training of RTNs that is based on a proposed classification loss. With RTNs, state-of-the-art performance is attained on several benchmarks for semantic correspondence.

Statics and Dynamics of Continuum Robots With General Tendon Routing and External Loading <=SEP=> Tendons are a widely used actuation strategy for continuum robots that enable forces and moments to be transmitted along the robot from base-mounted actuators. Most prior robots have used tendons routed in straight paths along the robot. However, routing tendons through general curved paths within the robot offers potential advantages in reshaping the workspace and enabling a single section of the robot to achieve a wider variety of desired shapes. In this paper, we provide a new model for the statics and dynamics of robots with general tendon routing paths that is derived by coupling the classical Cosserat-rod and Cosserat-string models. This model also accounts for general external loading conditions and includes traditional axially routed tendons as a special case. The advantage of the usage of this coupled model for straight-tendon robots is that it accounts for the distributed wrenches that tendons apply along the robot. We show that these are necessary to consider when the robot is subjected to out-of-plane external loads. Our experimental results demonstrate that the coupled model matches experimental tip positions with an error of 1.7% of the robot length, in a set of experiments that include both straight and nonstraight routing cases, with both point and distributed external loads.

Folded dipole antenna near metal plate <=SEP=> The paper presents the effects on antenna parameters when an antenna is placed horizontally near a metal plate. The plate has finite size and rectangular shape. A folded dipole antenna is used and it is placed symmetrically above the plate. The FEM (finite element method) is used to simulate the dependency of antenna parameters on the size of the plate and the distance between the plate and the antenna. The presence of the metal plate, even a small one if it is at the right distance, causes very big changes in the behaviour of the antenna. The bigger the plate, especially in width, the sharper and narrower are the lobes of the radiation pattern. The antenna height defines how many lobes the radiation pattern has. A number of the antenna parameters, including impedance, directivity and front-to-back ratio, change periodically as the antenna height is increased. The resonant frequency of the antenna also changes under the influence of the metal plate.

Semi-supervised Learning by Entropy Minimization <=SEP=> We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regularization, which enables to incorporate unlabeled data in the standard supervised learning. Our approach includes other approaches to the semi-supervised problem as particular or limiting cases. A series of experiments illustrates that the proposed solutions benefit from unlabeled data. The method challenges mixture models when the data are sampled from the distribution class spanned by the generative model. The performances are definitely in favor of minimum entropy regularization when generative models are misspecified, and the weighting of unlabeled data provides robustness to the violation of the “cluster assumption”. Finally, we also illustrate that the method can also be far superior to manifold learning in high dimension spaces.

LinkNBed: Multi-Graph Representation Learning with Entity Linkage <=SEP=> Knowledge graphs have emerged as an important model for studying complex multirelational data. This has given rise to the construction of numerous large scale but incomplete knowledge graphs encoding information extracted from various resources. An effective and scalable approach to jointly learn over multiple graphs and eventually construct a unified graph is a crucial next step for the success of knowledge-based inference for many downstream applications. To this end, we propose LinkNBed, a deep relational learning framework that learns entity and relationship representations across multiple graphs. We identify entity linkage across graphs as a vital component to achieve our goal. We design a novel objective that leverage entity linkage and build an efficient multi-task training procedure. Experiments on link prediction and entity linkage demonstrate substantial improvements over the state-ofthe-art relational learning approaches.

Metacognition of the testing effect: guiding learners to predict the benefits of retrieval. <=SEP=> If the mnemonic benefits of testing are to be widely realized in real-world learning circumstances, people must appreciate the value of testing and choose to utilize testing during self-guided learning. Yet metacognitive judgments do not appear to reflect the enhancement provided by testing Karpicke & Roediger (Science 319:966-968, 2008). In this article, we show that under judicious conditions, learners can indeed reveal an understanding of the beneficial effects of testing, as well as the interaction of that effect with delay (experiment 1). In that experiment, subjects made judgments of learning (JOLs) for previously studied or previously tested items in either a cue-only or a cue-target context, and either immediately or after a 1-day delay. When subjects made judgments in a cue-only context, their JOLs accurately reflected the effects of testing, both immediately and at a delay. To evaluate the potential of exposure to such conditions for promoting generalized appreciation of testing effects, three further experiments elicited global predictions about restudied and tested items across two study/test cycles (experiments 2, 3, and 4). The results indicated that learners' global naïve metacognitive beliefs increasingly reflect the beneficial effects of testing when learners experience these benefits with increasing external support. If queried under facilitative circumstances, learners appreciate the mnemonic enhancement that testing provides on both an item-by-item and global basis but generalize that knowledge to future learning only with considerable guidance.

White-box cryptography: practical protection on hostile hosts <=SEP=> Businesses often interact with users via web-browsers and applications on mobile devices, and host services on cloud servers they may not own. Such highly-exposed environments employ white-box cryptography (WBC) for security protection. WBC operates on a security model far different from the traditional black-box model. The modern business world includes large commercial segments in which end-users are directly exposed to business application software hosted on web browsers, mobile phones, web-connected tablets, and an increasing number of other devices: the `internet of things' (IoT). Software applications and their communication activities now dominate much of the commercial world, and there have been countless hacks on such software, and on devices hosting it, with targets as diverse as mobile phones, web browser applications, vehicles, and even refrigerators! The business advantages of deploying computational power near the user encourage software migration to exposed network end-points, but this increasing exposure provides an ever growing attack surface. Here, we discuss goals and challenges of white-box cryptography and emerging approaches in a continual attempt to stay at least one step ahead of the attackers. We list some WBC techniques, both traditional and recent, indicating how they might be incorporated into a WBC AES implementation.

The pursuit of happiness: time, money, and social connection. <=SEP=> Does thinking about time, rather than money, influence how effectively individuals pursue personal happiness? Laboratory and field experiments revealed that implicitly activating the construct of time motivates individuals to spend more time with friends and family and less time working-behaviors that are associated with greater happiness. In contrast, implicitly activating money motivates individuals to work more and socialize less, which (although productive) does not increase happiness. Implications for the relative roles of time versus money in the pursuit of happiness are discussed.

Fragile online relationship: a first look at unfollow dynamics in twitter <=SEP=> We analyze the dynamics of the behavior known as 'unfollow' in Twitter. We collected daily snapshots of the online relationships of 1.2 million Korean-speaking users for 51 days as well as all of their tweets. We found that Twitter users frequently unfollow. We then discover the major factors, including the reciprocity of the relationships, the duration of a relationship, the followees' informativeness, and the overlap of the relationships, which affect the decision to unfollow. We conduct interview with 22 Korean respondents to supplement the quantitative results.
 They unfollowed those who left many tweets within a short time, created tweets about uninteresting topics, or tweeted about the mundane details of their lives. To the best of our knowledge, this work is the first systematic study of the unfollow behavior in Twitter.

The role of emotion in computer-mediated communication: A review <=SEP=> It has been argued that the communication of emotions is more difficult in computer-mediated communication (CMC) than in face-to-face (F2F) communication. The aim of this paper is to review the empirical evidence in order to gain insight in whether emotions are communicated differently in these different modes of communication. We review two types of studies: (1) studies that explicitly examine discrete emotions and emotion expressions, and (2) studies that examine emotions more implicitly, namely as self-disclosure or emotional styles. Our conclusion is that there is no indication that CMC is a less emotional or less personally involving medium than F2F. On the contrary, emotional communication online and offline is surprisingly similar, and if differences are found they show more frequent and explicit emotion communication in CMC than in F2F. 2007 Elsevier Ltd. All rights reserved.

Initial construction and validation of the Pathological Narcissism Inventory. <=SEP=> The construct of narcissism is inconsistently defined across clinical theory, social-personality psychology, and psychiatric diagnosis. Two problems were identified that impede integration of research and clinical findings regarding narcissistic personality pathology: (a) ambiguity regarding the assessment of pathological narcissism vs. normal narcissism and (b) insufficient scope of existing narcissism measures. Four studies are presented documenting the initial derivation and validation of the Pathological Narcissism Inventory (PNI). The PNI is a 52-item self-report measure assessing 7 dimensions of pathological narcissism spanning problems with narcissistic grandiosity (Entitlement Rage, Exploitativeness, Grandiose Fantasy, Self-sacrificing Self-enhancement) and narcissistic vulnerability (Contingent Self-esteem, Hiding the Self, Devaluing). The PNI structure was validated via confirmatory factor analysis. The PNI correlated negatively with self-esteem and empathy, and positively with shame, interpersonal distress, aggression, and borderline personality organization. Grandiose PNI scales were associated with vindictive, domineering, intrusive, and overly-nurturant interpersonal problems, and vulnerable PNI scales were associated with cold, socially avoidant, and exploitable interpersonal problems. In a small clinical sample, PNI scales exhibited significant associations with parasuicidal behavior, suicide attempts, homicidal ideation, and several aspects of psychotherapy utilization.

COPSS-lite: Lightweight ICN Based Pub/Sub for IoT Environments <=SEP=> Information Centric Networking (ICN) is a new networking paradigm that treats content as the first class entity. It provides content to users without regards to the current location or source of the content. The publish/subscribe (pub/sub) systems have gained popularity in Internet. Pub/sub systems dismisses the need for users to request every content of their interest. Instead, the content is supplied to the interested users (subscribers) as and when it is published. CCN/NDN are popular ICN proposals widely accepted in the ICN community however, they do not provide an efficient pub/sub mechanism. COPSS enhances CCN/NDN with an efficient pub/sub capability. Internet of Things (IoT) is a growing topic of interest in both Academia and Industry. The current designs for IoT relies on IP. However, the IoT devices are constrained in their available resources and IP is heavy for their operation.We observed that IoT’s are information centric in nature and hence ICN is a more suitable candidate to support IoT environments. Although NDN and COPSS work well for the Internet, their current full fledged implementations cannot be used by the resource constrained IoT devices. CCN-lite is a light weight, inter-operable version of the CCNx protocol for supporting the IoT devices. However, CCN-lite like its ancestors lacks the support for an efficient pub/sub mechanism. In this paper, we developed COPSS-lite, an efficient and light weight implementation of pub/sub for IoT. COPSS-lite is developed to enhance CCN-lite and also support multi-hop connection by incorporating the famous RPL protocol for low power and lossy networks. We provide a preliminary evaluation to show proof of operability with real world sensor devices in IoT lab. Our results show that COPSS-lite is compact, operates on all platforms that support CCN-lite and we observe significant performance benefits with COPSS-lite in IoT environments.

Parsing English with a Link Grammar <=SEP=> We develop a formal grammatical system called a link grammar, show how English grammar can be encoded in such a system, and give algorithms for efficiently parsing with a link grammar. Although the expressive power of link grammars is equivalent to that of context free grammars, encoding natural language grammars appears to be much easier with the new system. We have written a program for general link parsing and written a link grammar for the English language. The performance of this preliminary system – both in the breadth of English phenomena that it captures and in the computational resources used – indicates that the approach may have practical uses as well as linguistic significance. Our program is written in C and may be obtained through the internet. c 1991 Daniel Sleator and Davy Temperley * School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, sleator@cs.cmu.edu. y Music Department, Columbia University, New York, NY 10027, dt3@cunixa.cc.columbia.edu. Research supported in part by the National Science Foundation under grant CCR-8658139, Olin Corporation, and R. R. Donnelley and Sons. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of Olin Corporation, R. R. Donnelley and Sons, or the NSF.

Robust Physical-World Attacks on Machine Learning Models <=SEP=> Deep neural network-based classifiers are known to be vulnerable to adversarial examples that can fool them into misclassifying their input through the addition of small-magnitude perturbations. However, recent studies have demonstrated that such adversarial examples are not very effective in the physical world—they either completely fail to cause misclassification or only work in restricted cases where a relatively complex image is perturbed and printed on paper. In this paper we propose a new attack algorithm—Robust Physical Perturbations (RP2)— that generates perturbations by taking images under different conditions into account. Our algorithm can create spatiallyconstrained perturbations that mimic vandalism or art to reduce the likelihood of detection by a casual observer. We show that adversarial examples generated by RP2 achieve high success rates under various conditions for real road sign recognition by using an evaluation methodology that captures physical world conditions. We physically realized and evaluated two attacks, one that causes a Stop sign to be misclassified as a Speed Limit sign in 100% of the testing conditions, and one that causes a Right Turn sign to be misclassified as either a Stop or Added Lane sign in 100% of the testing conditions.

Sociable driving agents to maintain driver's attention in autonomous driving <=SEP=> Recently, many studies have been conducted on increasing the automation level in cars to achieve safer and more efficient transportation. The increased automation level creates room for the drivers to shift their attention to non-driving related activities. However, there are cases that cannot be handled by automation where a driver should take over the control. This pilot study investigates a paradigm for keeping the drivers' situation-awareness active during autonomous driving by utilizing a social robot system, NAMIDA. NAMIDA is an interface consisting of three sociable driving agents that can interact with the driver through eye-gaze behaviors. We analyzed the effectiveness of NAMIDA on maintaining the drivers' attention to the road, by evaluating the response time of the drivers to a critical situation on the road. An experiment consisting of a take over scenario was conducted in a dynamic driving simulator. The results showed that existence of NAMIDA significantly reduced the response time of the drivers. However, surprisingly, NAMIDA without eye-gaze behaviors was more effective in reducing the response time than NAMIDA with eye-gaze behaviors. Additionally, the results revealed better subjective impressions for NAMIDA with eye-gaze behaviors behaviors.