nils-herrmann/clean_scirepeval huggingface dataset:
A self-adaptation framework for resource constrained miniature search and rescue robots

The size of miniature urban search and rescue robots forces constraints on critical resources such as power, space, and computational ability. To alleviate the effect of resource constraints, it is prudent to let robots adapt themselves autonomously in reaction to unforeseen conditions in disaster area. Software adaptation has been the goal of many researchers, but hardware adaptation is an important aspect, too. In this paper, we present a self-adaptation framework for heterogeneous multi-robot collectives, which consists of partial dynamic reconfiguration for hardware adaptation, module migration for software adaptation, and task performance evaluation to enable autonomous adaptation. These things taken together represent an Embedded Virtual Machine for hardware/software task migration. Our Morphing Crossbar structure, which interconnects reusable hardware modules, is presented to increase the partial dynamic reconfiguration performance. Combined with the runtime downloading and installation of reusable software modules based on the port-based object framework in our PBO/RT realtime operating system, high-performance, heterogeneous, runtime adaptation of hardware/software real-time computational systems is achieved. The adaptation evaluators provide the ability of robotic systems to self-diagnose and self-adapt with an appropriate, distributed configuration monitor. We have implemented this self-adaptation framework in our RecoNode CPU node. By using RecoNode-based CRAWLER and HexRotor robots, an application scenario is presented in order to show the feasibility of this framework.
Automatic classification of teeth in bitewing dental images

We present an automated algorithm to classify teeth in bitewing dental images, using Bayesian classification, and assign an absolute number to each tooth based on common numbering system used in dentistry. Fourier descriptors of the contours of the molar and the premolar teeth in bitewing images are used in the Bayesian classification of these two types of the teeth. Then, the spatial relation between the two types of the teeth is considered to number each tooth and correct the misclassification of some teeth in order to obtain high precision results. Experiments with 50 bitewing images containing more than 400 teeth show that our method is capable of classifying and assigning absolute index number to the teeth with high accuracy.
Automatic measurement of physical mobility in Get-Up-and-Go Test using kinect sensor

Get-Up-and-Go Test is commonly used for assessing the physical mobility of the elderly by physicians. This paper presents a method for automatic analysis and classification of human gait in the Get-Up-and-Go Test using a Microsoft Kinect sensor. Two types of features are automatically extracted from the human skeleton data provided by the Kinect sensor. The first type of feature is related to the human gait (e.g., number of steps, step duration, and turning duration); whereas the other one describes the anatomical configuration (e.g., knee angles, leg angle, and distance between elbows). These features characterize the degree of human physical mobility. State-of-the-art machine learning algorithms (i.e. Bag of Words and Support Vector Machines) are used to classify the severity of gaits in 12 subjects with ages ranging between 65 and 90 enrolled in a pilot study. Our experimental results show that these features can discriminate between patients who have a high risk for falling and patients with a lower fall risk.
An emotion recognition comparative study of autistic and typically-developing children using the zeno robot

In this paper we present the results of our recent study on comparing the emotion expression recognition abilities of children diagnosed with high functioning Autism (ASD) with those of typically developing (TD) children through use of a humanoid robot, Zeno. In our study we investigated the effect of incorporating gestures to the emotion expression prediction accuracy of both child groups. Although the idea that ASD individuals suffer from general emotion recognition deficits is widely assumed [1], we found no significant impairment in the general emotion prediction. However, a specific deficit in correctly identifying Fear was found for children with Autism when compared to the TD children. Furthermore, we found that gestures can significantly impact the prediction accuracy of both ASD and TD children in a negative or positive manner depending on the specific expression. Thus, the use of gestures for conveying emotional expressions by a humanoid robot in a social skill therapy setting is relevant. The methodology and experimental protocol are presented and additional discussion of the Zeno R-50 robot used is given.
Bidirectional Warping of Active Appearance Model

Active Appearance Model (AAM) is a commonly used method for facial image analysis with applications in face identification and facial expression recognition. This paper proposes a new approach based on image alignment for AAM fitting called bidirectional warping. Previous approaches warp either the input image or the appearance template. We propose to warp both the input image, using incremental update by an affine transformation, and the appearance template, using an inverse compositional approach. Our experimental results on Multi-PIE face database show that the bidirectional approach outperforms state-of-the-art inverse compositional fitting approaches in extracting landmark points of faces with shape and pose variations.
Can NAO Robot Improve Eye-Gaze Attention of Children with High Functioning Autism?

Presents the results of a preliminary study on understanding how humanoid robots can successfully improve social and communication skills among children with Autism Spectrum Disorders (ASD). Children with ASD experience deficits in appropriate verbal and nonverbal communication skills including motor control, emotional facial expressions, eye-gaze attention, and joint attention. Studies have shown that positive feedback from the robot on the participants' performance is an effective way to encourage children with ASD to communicate more. Other studies have also examined the use of affect recognition based on psycho physiological responses to modify the behaviors during a robotic game. However, there is limited information on the utility of humanoid robots' positive.
Disparity-Based 3D Face Modeling for 3D Face Recognition

We present an automatic disparity-based approach for 3D face modeling, from two frontal and one profile view stereo images, for 3D face recognition applications. Once the images are captured, the algorithm starts by extracting selected 2D facial features from one of the frontal views and computes a dense disparity map from the two frontal images. We then align a low resolution 2D mesh model to the selected features, adjust some of its vertices along the profile line using the profile view, increase its triangular vertices to a higher resolution, and re-project them back on the frontal image. Using the coordinates of the re-projected vertices and their corresponding disparities, we capture and compute the 3D facial shape variations using stereo vision. The final result is a deformed 3D model specific to a given subject's face. Application of the model in 3D face recognition validates the algorithm and shows a promising 98 % recognition rate.
An Adaptive Bayesian Source Separation Method for Intensity Estimation of Facial AUs

Automated measurement of the intensity of spontaneous facial Action Units (AU) defined by the Facial Action Coding System (FACS) in video sequences is a challenging problem. This paper proposes a person-adaptive methodology for the intensity estimation of spontaneous AUs. We formulate this problem as a source separation problem where we consider the observed AUs as the source signals to be separated from each other and other information given by a sequence of facial images. We first compute an initial estimation of the sources, called observations, using sparse linear regression functions. We then develop and apply a Bayesian source separation method that recruits the prior information of the sources to iteratively improve the initial estimations/observations in an adaptive fashion. Furthermore, our approach adaptively uses some testing information (but not the ground-truth labels) to improve the performance of the approach (i.e., Person-Adaptive model). Our experimental results on DISFA, UNBC-McMaster and FERA2015 databases show that this approach is very promising for automated measurement of the intensity of spontaneous facial AUs.
Development of a Wearable Sensor System for Measuring Body Joint Flexion

This paper presents a novel approach for measuring and monitoring human body joint angles using wearable sensors. This type of monitoring is beneficial for therapists and physicians as it allows them to assess patients' activities remotely. In our approach multiple flex-sensors are mounted on supportive cloth to measure the flexion of a joint. The changes in the resistivity of the flex-sensors are measured using an electronic board. We utilize an Extended Kalman Filter (EKF) to predict the joint angle based on the dynamic model of the joint movement and the measurements obtained from the flex-sensors. Due to variations in the measured angle by each sensor, the outputs are fussed to reduce the error and estimate the best value for the actual body joint angle. We evaluated the effectiveness and performance of our approach for measuring knee joint angle by comparing with the measured angles using goniometer. The result shows that the average of error is 6.92Ë with correlation of 0.98.
eBear: An expressive Bear-Like robot

This paper presents an anthropomorphic robotic bear for the exploration of human-robot interaction including verbal and non-verbal communications. This robot is implemented with a hybrid face composed of a mechanical faceplate with 10 DOFs and an LCD-display-equipped mouth. The facial emotions of the bear are designed based on the description of the Facial Action Coding System as well as some animal-like gestures described by Darwin. The mouth movements are realized by synthesizing emotions with speech. User acceptance investigations have been conducted to evaluate the likability of these facial behaviors exhibited by the eBear. Multiple Kernel Learning is proposed to fuse different features for recognizing user's facial expressions. Our experimental results show that the developed Bear-Like robot can perceive basic facial expressions and provide emotive conveyance towards human beings.
An FFT-based synchronization approach to recognize human behaviors using STN-LFP signal

Classification of human behavior is a key step to developing closed-loop Deep Brain Stimulation (DBS) systems, which may decrease the power consumption and side effects of the existing systems. Recent studies have shown that the Local Field Potential (LFP) signals from both Subthalamic Nuclei (STN) of the brain can be used to recognize human behavior. Since the DBS leads implanted in each STN can collect three bipolar signals, the selection of a suitable pair of LFPs that achieves optimal recognition performance is still an open problem to address. Considering the presence of synchronized aggregate activity in the basal ganglia, this paper presents an FFT-based synchronization approach to automatically select a relevant pair of LFPs and use the pair together with an SVM-based MKL classifier for behavior recognition purposes. Our experiments on five subjects show the superiority of the proposed approach compared to other methods used for behavior classification.
Bidirectional warping of Active Appearance Model

Active Appearance Model (AAM) is a commonly used method for facial image analysis with applications in face identification, tracking, and expression recognition. This paper proposes a new approach for AAM fitting. Our approach is called bidirectional warping and is based on image alignment which simultaneously warps both input image and the appearance model when fitting AAM into the input image. Our bidirectional warping technique makes the fitting algorithm less sensitive to initial condition and the model can fit into faces that are never seen or included in the training set. Our experimental results show that our approach outperforms state-of-the-art AAM fitting techniques particularly when the model is far from ground truth.
Comparing the gaze responses of children with autism and typically developed individuals in human-robot interaction

Robots are becoming a part of humans' social life as assistants, companionbots, therapists, and entertainers. One promising application of the socially assistive robots is in autism therapy, where robots are employed to enhance verbal and nonverbal skills (e.g. eye-gaze attention, facial expression mimicry) of individuals with Autism Spectrum Disorder (ASD). One important question is "How the gaze responses of individuals with ASD differ from that of Typically Developing (TD) peers when interacting with a robot?" We present the results of our recent studies for modeling and analyzing the gaze pattern of children with ASD when they interact with a robot called NAO. This paper reports the differences of gaze responses of TD and ASD group in two conversational contexts: Speaking versus Listening. We used Variable-order Markov Model (VMM) to discover the temporal gaze directional patterns of ASD and TD groups. The results reveal that the gaze responses of the TD individuals in speaking and listening contexts, can be best modeled by VMM with order zero and three, respectively. As we expected, the result show that the temporal gaze patterns of typically developed children are varying when the role in the conversational context is changed. However for the ASD individuals for both conversational contexts the VMM with order one could best fit the data. Overall, the results conclude that VMM is a powerful technique to model different gaze responses of TD and ASD individuals in speaking and listening contexts.
Image-Based Fall Detection with Human Posture Sequence Modeling

In this paper, an image-based method is presented for fall detection using statistical human posture sequence modeling. Specifically, a series of laboratory simulated falls and activities of daily living (ADLs) are performed and recorded by a Kinect sensor as training video data. The skeleton view of a human body in these video recordings is extracted using the Kinect for Windows SDK. Hidden Markov Models are used for modeling the fall posture sequences and distinguishing different fall activities and ADLs. Our experimental results demonstrate an average fall recognition rate above 80% and the capability of early warning for falls.
Multi-modal (2-D and 3-D) face modeling and recognition using Attributed Relational Graph

In this paper we present a unified graph model, called Attributed Relational Graph (ARG), for multi-modal face modeling and recognition. Based on the ARG model, the 2-D and 3-D data are included in a single model. The developed ARG model consists of nodes, edges, and mutual relations. The nodes of the graph correspond to the landmark points that are extracted by an improved Active Shape Model (ASM) technique. Then, at each node of the graph, the responses of a set of log-Gabor filters to the facial image texture and shape information (depth values) are calculated; the filter responses are used to model the local structure of the face at each node of the graph. The edges of the graph are defined based on Delaunay triangulation and a set of mutual relations between the sides of the triangles are defined. The mutual relations boost the final performance of the system. The results of face matching using the 2-D and 3-D attributes and the mutual relations are fused at the score level. A rank-one identification rate of 99% is achieved by experimenting on the University of Miami face database.
Multi-modal ear and face modeling and recognition

In this paper we describe a multi-modal ear and face biometric system. The system is comprised of two components: a 3D ear recognition component and a 2D face recognition component. For the 3D ear recognition, a series of frames is extracted from a video clip and the region of interest (i.e., ear) in each frame is independently reconstructed in 3D using Shape From Shading. The resulting 3D models are then registered using the iterative closest point algorithm. We iteratively consider each model in the series as a reference model and calculate the similarity between the reference model and every model in the series using a similarity cost function. Cross validation is performed to assess the relative fidelity of each 3D model. The model that demonstrates the greatest overall similarity is determined to be the most stable 3D model and is subsequently enrolled in the database. For the 2D face recognition, a set of facial landmarks is extracted from frontal facial images using the Active Shape Model. Then, the response of facial images to a series of Gabor filters at the locations of facial landmarks are calculated. The Gabor features (attributes) are stored in the database as the face model for recognition. The similarity between the Gabor features of a probe facial image and the reference models are utilized to determine the best match. The match scores of the ear recognition and face recognition modalities are fused to boost the overall recognition rate of the system. Experiments are conducted using a gallery set of 402 video clips and a probe of 60 video clips (images). As a result, a rank-one identification rate of 100% was achieved using the weighted sum technique for fusion.
A unified probabilistic framework for measuring the intensity of spontaneous facial action units

Automatic facial expression analysis has received great attention in both academia and industry in the past two decades. Facial action coding system, which describes all possible facial expressions based on a set of anatomical facial muscle movements, called Action Unit (AU), is the most popularly used descriptive approach for analyzing facial expressions. In majority of the existing studies in the area of facial expression recognition, the focus has mostly been on facial action unit detection or basic facial expression recognition and there have been very few works on investigating the measuring the intensity of spontaneous facial actions. In addition, these works try to measure the intensity of facial actions statically and individually, ignoring the dependence among AUs, as well as the temporal information, which is crucial for analyzing spontaneous expression. To overcome this problem, this paper proposes a framework based on Dynamic Bayesian Network (DBN) to systematically model such relationships among spontaneous AUs for measuring their intensities. Our experimental results show improvement over image-driven methods alone in AU intensity measurement.
Localized support vector machines using Parzen window for incomplete sets of categories

This paper describes a novel approach to pattern classification that combines Parzen window and support vector machines. Pattern classification is usually performed in universes where all possible categories are defined. Most of the current supervised learning classification techniques do not account for undefined categories. In a universe that is only partially defined, there may be objects that do not fall into the known set of categories. It would be a mistake to always classify these objects as a known category. We propose a Parzen window-based approach which is capable of classifying an object as not belonging to a known class. In our approach we use a Parzen window to identify local neighbors of a test point and train a localized support vector machine on the identified neighbors. Visual category recognition experiments are performed to compare the results of our approach, localized support vector machines using a k-nearest neighbors approach, and global support vector machines. Our experiments show that our Parzen window approach has superior results when testing with incomplete sets, and comparable results when testing with complete sets.
ReFrESH: A self-adaptation framework to support fault tolerance in field mobile robots

Mobile robots are being employed far more often in extreme environments, such as urban search and rescue, with greater levels of autonomy; yet recent studies on field robotics show that numerous failure modes affect the reliability of the robot in meeting mission objectives. Therefore, fault tolerance is increasingly important for field robots operating in unpredictable environments to ensure safety and effectiveness of the system. This paper demonstrates a self-adaptation framework, ReFrESH, that contains mechanisms for fault detection and fault mitigation. The goal of ReFrESH is to provide diagnosable and maintainable infrastructure support, built into a real-time operating system, to manage task performance in the presence of unexpected uncertainties. ReFrESH augments the port-based object framework by attaching evaluation and estimation mechanisms to each functional component so that the robot can easily detect and locate faults. In conjunction, a task level decision mechanism interacts with the fault detection elements in order to generate and choose an optimal approach to mitigating faults. Moreover, to increase flexibility of the fault tolerance, ReFrESH provides self-adaptation support for both software and hardware functionality. To our knowledge, this is the first framework to support both software and hardware self-adaptation. A demonstrative application of ReFrESH illustrates its applicability through a target tracking task deployed on a mobile robot system.
Social risk and depression: Evidence from manual and automatic facial expression analysis

Investigated the relationship between change over time in severity of depression symptoms and facial expression. Depressed participants were followed over the course of treatment and video recorded during a series of clinical interviews. Facial expressions were analyzed from the video using both manual and automatic systems. Automatic and manual coding were highly consistent for FACS action units, and showed similar effects for change over time in depression severity. For both systems, when symptom severity was high, participants made more facial expressions associated with contempt, smiled less, and those smiles that occurred were more likely to be accompanied by facial actions associated with contempt. These results are consistent with the “social risk hypothesis” of depression. According to this hypothesis, when symptoms are severe, depressed participants withdraw from other people in order to protect themselves from anticipated rejection, scorn, and social exclusion. As their symptoms fade, participants send more signals indicating a willingness to affiliate. The finding that automatic facial expression analysis was both consistent with manual coding and produced the same pattern of depression effects suggests that automatic facial expression analysis may be ready for use in behavioral and clinical science.