nils-herrmann/clean_scirepeval huggingface dataset:
EgoMaster: A central ego motion estimation for driver assist systems

In this paper we present an approach for a central ego motion estimation with standard and near-series sensors for advanced driver assist systems. The two main contributions of this article are the provision of all variables relative to the road surface and the fusion of several sensors and perception modules providing information of ego localization. Thus we employ a discrete Kalman filter to consider the standard lift sensors of the suspension and to take the radial tire deflection into account. Additionally, a reference sensor set is introduced which enables to evaluate all measured and estimated states, even those which are not related to the vector of gravity. The validation by the reference sensor set demonstrates the accurrancy and the efficiency of the approach.
Tracking control with adaptively Allocated Maximum Input Amplitudes and Enlarged Domain of Attraction for linear systems

We propose a novel method for designing a fast trajectory tracking controller based on a 2-DOF control structure for linear systems subject to input constraints. Major steps are: First, we derive a required decaying rate of a quadratic Lyapunov function (QLF) that enables an adaptation of the maximum input amplitudes reserved for error compensation. This adaptation is based on the current feedforward signal. Second, we show a saturating control law which ensures the existence of such a QLF. The corresponding domain of attraction is estimated by solving a convex optimization problem. Third, we add a Lyapunov-based command governor in order to further enlarge the controller's domain of attraction. Simulation and experimental results confirm the performance benefit of the proposed method.
Robotic binaural localization and separation of more than two concurrent sound sources

We present a new binaural sound-source separation and localization technique for the underdetermined case where the present sound sources outnumber the available microphones. The proposed technique has access to a generic set of head-related transfer functions (HRTFs) and processes input signals obtained from two small microphones placed inside the ear canals of a robot humanoid head equipped with artificial ears and mounted on a torso. By exploiting sparse representations of the ear input signals, the 3D position of three concurrent sound sources is extracted by identifying the HRTFs that have filtered the sound signals. The sought HRTFs are estimated using a well-known self-splitting competitive learning technique for clustering. Simulation results demonstrated the performance of the new technique in localizing both azimuth and elevation angles for three concurrently active sound sources. The proposed method relies purely on auditive cues and provides an easy implementation on robotic platforms.
Automatic Model-Order Selection for PCA

Determining the model-order of a given data set is an important task in signal analysis. Principal component analysis (PCA) can be used for this purpose if there is a criterion upon which the correct order can be chosen. In this work, we propose a new and simple technique to determine automatically the rank of a PCA model. Tested with simulated data, the algorithm is able to determine the correct model order efficiently. Applied to video sequences, this method is able to estimate the necessary subspaces that capture the motion and illuminance changes within the different frames. This helps in reducing the storage need/requirements of video sequences and improves the efficiency of context based search and retrieval techniques.
A new algorithm for gene mapping: Application of partial least squares regression with cross model validation

Identifying the causal genetic markers responsible for certain phenotypes is a main aim in human genetics. In the context of complex diseases, which are believed to have multiple causal loci of largely unknown effects and positions, it is essential to formulate general yet accurate methods for gene mapping. In this direction of research, a new algorithm for gene mapping is proposed which treats the data using partial least squares regression and then locates the causal markers by cross model validation. Results obtained show their compliance with the ones obtained by standard techniques, yet more accuracy is achieved; hence, showing another application of multi-variate data analysis to the problem of human genetics.
An Enhanced Binaural 3D Sound Localization Algorithm

For sound localization methods to be useful in real-time scenarios, the processing power requirements must be low enough to allow real time processing of audio inputs. We propose a new binaural sound source localization technique based on using only two microphones placed inside the ear canal of a robot dummy head. The head is equipped with artificial ears and is mounted on a torso. In contrast to existing 3D sound source localization methods using microphone arrays, our novel method employs two microphone and is based on a simple correlation approach using a generic set of head related transfer functions (HRTFs). The proposed method is demonstrated through simulation and is further tested in a household environment. This set up proves to be very noise-tolerant and is able to localize sound sources in free space with high precision and low computational complexity
Video is a Cube

Quality of experience (QoE) is becoming increasingly important in signal processing applications. In taking inspiration from chemometrics, we provide an introduction to the design of video quality metrics by using data analysis methods, which are different from traditional approaches. These methods do not necessitate a complete understanding of the human visual system (HVS). We use multidimensional data analysis, an extension of well-established data analysis techniques, allowing us to better exploit higher-dimensional data. In the case of video quality metrics, it enables us to exploit the temporal properties of video more properly; the complete three-dimensional structure of the video cube is taken into account in metrics' design. Starting with the well-known principal component analysis and an introduction to the notation of multiway arrays, we then present their multidimensional extensions, delivering better quality prediction results. Although we focus on video quality, the presented design principles can easily be adapted to other modalities and to even higher dimensional data sets as well.
Saliency based video quality prediction using multi-way data analysis

Saliency information allows us to determine which parts of an image or video frame attracts the focus of the observer and thus where distortions will be more obvious. Using this knowledge and saliency thresholds, we therefore combine the saliency information generated by a computational model and the features extracted from the H.264/AVC bitstream, and use the resulting saliency-weighted features in the design of a video quality metric with multi-way data analysis. We used two different multi-way methods, the two dimensional principal component regression (2D-PCR) and multi-way partial least squares regression (PLSR) in the design of a no-reference video quality metric, where the different saliency levels are considered as an additional direction. Our results show that the consideration of the the saliency information leads to more stable models with less parameters in the model and thus the prediction performance increases compared to metrics without saliency information for the same number of parameters.
Dynamic Substitution Model

In this paper, we present the Dynamic Substitution Model (DSM) and its variant the Static Substitution Model (SSM). In DSM and SSM, the secret encryption key is divided into a primary key and a secondary key. DSM is a model that allows any block cipher to accept a variable length secondary key, this is achieved by substituting some bits of the cipher's expanded key with the secondary key. SSM is a variant of DSM, where the secondary key length and the positions of the replaced bits of the subkeys are determined in the design time. We used the Advanced Encryption Standard (AES) to demonstrate the usage of DSM and SSM models.
Visual quality of current coding technologies at high definition IPTV bitrates

High definition video over IP based networks (IPTV) has become a mainstay in today's consumer environment. In most applications, encoders conforming to the H.264/AVC standard are used. But even within one standard, often a wide range of coding tools are available that can deliver a vastly different visual quality. Therefore we evaluate in this contribution different coding technologies, using different encoder settings of H.264/AVC, but also a completely different encoder like Dirac. We cover a wide range of different bitrates from ADSL to VDSL and different content, with low and high demand on the encoders. As PSNR is not well suited to describe the perceived visual quality, we conducted extensive subject tests to determine the visual quality. Our results show that for currently common bitrates, the visual quality can be more than doubled, if the same coding technology, but different coding tools are used.
Learning to walk with prior knowledge

In this work a novel approach to Transfer Learning for the use in Deep Reinforcement Learning is introduced. The agent is realized as an actor-critic framework, namely the Deep Deterministic Policy Gradient algorithm. The Q-function and the policy are represented as deep feed-forward networks, that are trained by minimizing the mean squared Bellman error and by maximizing the expected reward, respectively. For Transfer Learning, the actor is modified with a new regularization term, called the knowledge regularizer. It allows to include prior knowledge in from of an existing policy in the learning process. The knowledge regularizer shifts the current weight vector during the gradient descent step towards a region of the weight space, that is centered around the existing policy. Because neural networks are universal and smooth function approximators, the weights of the existing policy and the new ones have to lie close to each other in the weight space. Solving a task therefore benefits from the prior knowledge, when it is used to manipulate the gradient given by the critic. We could experimentally verify, that the knowledge regularizer results in a higher performance achieved by the agent and in a reduction of the learning time. Furthermore, the knowledge regularizer can be used as a replacement for labeled training data, which renders it especially useful for physical applications.
Transient probabilistic recurrent fuzzy systems

A probabilistic-based extension of recurrent fuzzy systems is presented and exemplarily applied to modeling and control of systems in different domains. The system's core-dynamic is described by a recurrent fuzzy system, while further known influencing features are summarized via probability theory using a stochastic automaton. The appropriate conditional probabilities are used to adapt the dynamics of the recurrent fuzzy system depending on its state variables. By allowing transient conditional probabilities, a time-variance is simultaneously achieved. Thus, the developed transient probabilistic recurrent fuzzy system (TP-RFS) is able to handle two kinds of uncertain information (vague and stochastic) and allows slight as well as drastic adaptations of the original recurrent fuzzy system's dynamics. Successful applications of the proposed TP-RFS for modeling different dynamics of an ecological system and for controlling the speed signaling on a highway are shown.
Best Practices for QoE Crowdtesting: QoE Assessment With Crowdsourcing

Quality of Experience (QoE) in multimedia applications is closely linked to the end users' perception and therefore its assessment requires subjective user studies in order to evaluate the degree of delight or annoyance as experienced by the users. QoE crowdtesting refers to QoE assessment using crowdsourcing, where anonymous test subjects conduct subjective tests remotely in their preferred environment. The advantages of QoE crowdtesting lie not only in the reduced time and costs for the tests, but also in a large and diverse panel of international, geographically distributed users in realistic user settings. However, conceptual and technical challenges emerge due to the remote test settings. Key issues arising from QoE crowdtesting include the reliability of user ratings, the influence of incentives, payment schemes and the unknown environmental context of the tests on the results. In order to counter these issues, strategies and methods need to be developed, included in the test design, and also implemented in the actual test campaign, while statistical methods are required to identify reliable user ratings and to ensure high data quality. This contribution therefore provides a collection of best practices addressing these issues based on our experience gained in a large set of conducted QoE crowdtesting studies. The focus of this article is in particular on the issue of reliability and we use video quality assessment as an example for the proposed best practices, showing that our recommended two-stage QoE crowdtesting design leads to more reliable results.
A New Method for Binaural 3-D Localization Based on Hrtfs

A modern technique for robotic sound source detection using a dataset head-related transfer functions (HRTFs) is presented. To ensure fast detection, the HRTFs are reduced using three different techniques, namely; diffuse-field equalization, balanced model truncation, and principle component analysis. A new criterion introduced is to be satisfied by a set of output signals from the microphones of a dummy robot head. This criterion is then used to find the sound source location in accordance with the reduced HRTF datasets. The suggested method is verified through simulated examples and further tested in a household environment. This novel technique provides estimates of azimuth and elevation angles in free space by using only two microphones. It also uses a simple algorithm compared to the more complicated algorithms used in similar localization processes
The Pushdown Attack on AES

We present a new idea on chosen plaintext cryptanalysis, where we can bypass some of the cipher's encryption rounds at its beginning. To illustrate this idea, we developed the Pushdown attack. This attack can increase the strength of some chosen plaintext attacks. We applied the Pushdown attack on AES and was able to achieve a 6-round attack that requires only 2$^{11}$ chosen plaintexts, this reduces the chosen plaintexts needed by the Square attack with a factor of 2$^{21}$.
Step Detection and Parameterization for Gait Assessment Using a Single Waist-Worn Accelerometer

One of the major reasons why the elderly lose their ability to live independently at home is the decline in gait performance. A measure to assess gait performance using accelerometers is step counting. The main problem with most step detection algorithms is the loss of accuracy at low speeds (<; 0.8 m/s) which limits their use in frail elderly populations. In this paper, a step detection algorithm was developed and validated using data from 10 healthy adults and 21 institutionalized seniors, predominantly frail older adults. Data were recorded using a single waist-worn triaxial accelerometer as each of the subjects performed one 10-m-walk trial. The algorithm demonstrated high mean sensitivity (99 ± 1%) for gait speeds between 0.2-1.5 m/s. False positives were evaluated with a series of motion activities performed by one subject. These activities simulate acceleration patterns similar to those generated near the body's center of mass while walking in terms of amplitude signal and periodicity. Cycling was the activity which led to a higher number of false positives. By applying template matching, we reduced by 73% the number of false positives in the cycling activity and eliminated all false positives in the rest of activities. Using K-means clustering, we obtained two different characteristic step patterns, one for normal and one for frail walking, where particular gait events related to limb impacts and muscle flexions were recognized. The proposed system can help to identify seniors at high risk of functional decline and monitor the progress of patients undergoing exercise therapy interventions.
Real-time cognitive technical systems, a learning material processing system, social and future aspects

Most of the robots today are used in production systems and the potential for assistive robots is high. Therefore, this paper demonstrates an architecture for real-time cognitive technical systems that are applicable for assistive and production robots with at least two sensor data input streams and one actuator to be controlled. An application of this architecture can be demonstrated by using a robotic production system that learns from a human expert, using different dimensionality reduction and classifier techniques such as Isometric feature mapping, Support Vector Machines, and Fuzzy k-Nearest Neighbor classification. This paper also contains a social study on the impact of research in Artificial Intelligence (AI) and cognitive technical systems. The study offers a synopsis of the history of AI and forecasts the social impact that intelligent robotic assistance has on the areas of automotive, medical care, production systems, and private homes.
Beyond classical teleoperation: Assistance, cooperation, data reduction, and spatial audio

In this video we present a teleoperation system which is capable of solving complex tasks in human-sized wide area environments. The system consists of two mobile teleoperators controlled by two operators, and offers haptic, visual, and auditory feedback. The task examined here, consists of repairing a robot by removing a computer and replacing a defective hard-drive. To cope with the complexity of such a task, we go beyond classical teleoperation by integrating several advanced software algorithms into the system.
Diabetes60 — Inferring Bread Units From Food Images Using Fully Convolutional Neural Networks

In this paper we propose a challenging new computer vision task of inferring Bread Units (BUs) from food images. Assessing nutritional information and nutrient volume from a meal is an important task for diabetes patients. At the moment, diabetes patients learn the assessment of BUs on a scale of one to ten, by learning correspondence of BU and meals from textbooks. We introduce a large scale data set of around 9k different RGB-D images of 60 western dishes acquired using a Microsoft Kinect v2 sensor. We recruited 20 diabetes patients to give expert assessments of BU values to each dish based on several images. For this task, we set a challenging baseline using state-of-the-art CNNs and evaluated it against the performance of human annotators. In our work we present a CNN architecture to infer the depth from RGB-only food images to be used in BU regression such that the pipeline can operate on RGB data only and compare its performance to RGB-D input data. We show that our inferred depth maps from RGB images can replace RGB-D input data at high significance for the BU regression task. In its best configuration, our proposed method achieves a RMSE of 1.53 BUs using RGB and inferred depth. Considering the variability among the raters themselves of RMSE = 0.89, we can show that our baseline method with depth prediction can extract reasonable nutritional information from RGB image data only.
Recurrent Takagi-Sugeno fuzzy interpolation for switched linear systems and hybrid automata

A novel perspective of fuzzy control is introduced, by combining a continuous Takagi-Sugeno (TSFS) with a discrete-time recurrent fuzzy system (RFS). The developed hybrid dynamic recurrent Takagi-Sugeno fuzzy approach enables a recurrent rule base, which leads to a dynamical interpolation law between linear subsystems. The formalism is applicable to switched systems and hybrid automaton models. The stability concerning switched systems is ensured by a common quadratic Lyapunov function. Additionally, two multiple Lyapunov function-based stability relaxation conditions are shown. For the hybrid automaton case, practical stability is analyzed. The performance of the approach is validated by simulating a car distance control system (hybrid automaton) and an experimental application to an inverted pendulum (switched control).
Analysis Operator Learning and its Application to Image Reconstruction

Exploiting a priori known structural information lies at the core of many image reconstruction methods that can be stated as inverse problems. The synthesis model, which assumes that images can be decomposed into a linear combination of very few atoms of some dictionary, is now a well established tool for the design of image reconstruction algorithms. An interesting alternative is the analysis model, where the signal is multiplied by an analysis operator and the outcome is assumed to be sparse. This approach has only recently gained increasing interest. The quality of reconstruction methods based on an analysis model severely depends on the right choice of the suitable operator. In this paper, we present an algorithm for learning an analysis operator from training images. Our method is based on lp-norm minimization on the set of full rank matrices with normalized columns. We carefully introduce the employed conjugate gradient method on manifolds, and explain the underlying geometry of the constraints. Moreover, we compare our approach to state-of-the-art methods for image denoising, inpainting, and single image super-resolution. Our numerical results show competitive performance of our general approach in all presented applications compared to the specialized state-of-the-art techniques.
A cognitive system for autonomous robotic welding

Currently, there is a high demand for autonomous industrial production systems. This paper outlines the development of a cognitive system for autonomous robotic welding. This system is based on dimensionality reduction techniques and Support Vector Machines, allowing the system to learn to separate between acceptable and unacceptable welding results within one batch, and to transfer this ability to a batch with different workpiece properties. It does not aim at a complete and general relationship between all process variables and result quantities, since it has been demonstrated that this is not necessary to reduce significantly the costs of calibrating the welding system. The main objective is to examine a cognitive system that stabilizes robotic welding processes by learning how to improve at least one process steering variable. In order to evaluate and improve the cognitive system, an extensive experimental setup is realized and described. The ability to learn and autonomously adapt to changes in workpiece properties allows the system to reduce the time an expert needs, and relaxes the requirements with respect to workpiece tolerances.
Geometric algorithms for the non-whitened one-unit linear Independent Component Analysis problem

In this paper, we study the problem of one-unit linear Independent Component Analysis (ICA) without whitening. The FastICA algorithm is arguably the most popular algorithm for solving the whitened one-unit linear ICA problem. Although a modified FastICA has been already proposed to solve the non-whitened one-unit linear ICA problem, there is unfortunately no known analysis regarding its effectiveness and efficiency. In this work, the non-whitened FastICA algorithm is revisited and analyzed in the framework of geometric optimization algorithms. In this paper, a conjugate gradient (CG) algorithm for the non-whitened one-unit linear ICA problem is developed as well. Local convergence properties of both algorithms are discussed. Finally, local convergence performance of the algorithms is investigated by several numerical experiments.