nils-herrmann/clean_scirepeval huggingface dataset:
Human-Centered Feed-Forward Control of a Vehicle Steering System Based on a Driver's Path-Following Characteristics

To improve vehicle path-following performance and to reduce driver workload, a human-centered feed-forward control (HCFC) system for a vehicle steering system is proposed. To be specific, a novel dynamic control strategy for the steering ratio of vehicle steering systems that treats vehicle speed, lateral deviation, yaw error, and steering angle as the inputs and a driver's expected steering ratio as the output is developed. To determine the parameters of the proposed dynamic control strategy, drivers are classified into three types according to the level of sensitivity to errors, i.e., low, middle, and high. The proposed HCFC system offers a human-centered steering system (HCSS) with a tunable steering gain, which can assist drivers in tracking a given path with smaller steering wheel angles and change rate of the angle by adaptively adjusting steering ratio according to driver's path-following characteristics, reducing the driver's workload. A series of experiments of tracking the centerline of double lane change (DLC) are conducted in CarSim and three different types of drivers are subsequently selected to test in a portable driving simulator under a fixed-speed condition. The simulation and experiment results show that the proposed HCSS with the dynamic control strategy, as compared with the classical control strategy of steering ratio, can improve task performance by about 7% and reduce the driver's physical workload and mental workload by about 35% and 50%, respectively, when following the given path.
REEDMULTIMEDIA LEARNING Cognitive Architectures for Multimedia Learning

This article provides a tutorial overview of cognitive architectures that can form a theoretical foundation for designing multimedia instruction. Cognitive architectures include a description of memory stores, memory codes, and cognitive operations. Architectures that are relevant to multimedia learning include Paivio’s dual coding theory, Baddeley’s working memory model, Engelkamp’s multimodal theory, Sweller’s cognitive load theory, Mayer’s multimedia learning theory, and Nathan’s ANIMATE theory. The discussion emphasizes the interplay between traditional research studies and instructional applications of this research for increasing recall, reducing interference, minimizing cognitive load, and enhancing understanding. Tentative conclusions are that (a) there is general agreement among the different architectures, which differ in focus; (b) learners’ integration of multiple codes is underspecified in the models; (c) animated instruction is not required when mental simulations are sufficient; (d) actions must be meaningful to be successful; and (e) multimodal instruction is superior to targeting modality-specific individual differences.
Full STEAM ahead: Exactly sparse gaussian process regression for batch continuous-time trajectory estimation on SE(3)

This paper shows how to carry out batch continuous-time trajectory estimation for bodies translating and rotating in three-dimensional (3D) space, using a very efficient form of Gaussian-process (GP) regression. The method is fast, singularity-free, uses a physically motivated prior (the mean is constant body-centric velocity), and permits trajectory queries at arbitrary times through GP interpolation. Landmark estimation can be folded in to allow for simultaneous trajectory estimation and mapping (STEAM), a variant of SLAM.
MiRoR—Miniaturized Robotic Systems for Holistic In-Situ Repair and Maintenance Works in Restrained and Hazardous Environments

This letter presents the novel concept of a miniaturized robotized machine tool, Mini-RoboMach, consisting of a walking hexapod robot and a Slender Continuum Arm. By combining the mobility of the walking robot with the positioning accuracy of the machine tool, with its 24 + 25 degrees of freedom, camera-based calibration system, laser scanner, and two end-effectors of opposed orientations, the proposed system can provide a versatile tool for  in-situ work (e.g., repair) in hazardous/unreachable locations in large installations.
Proactive Location-Based Scheduling of Delay-Constrained Traffic over Fading Channels

In this paper, proactive resource allocation based on user location for point-to-point communication over fading channels is introduced, whereby the source must transmit a packet when the user requests it within a deadline of a single time slot. We introduce a prediction model in which the source predicts the request arrival $T_p$ slots ahead, where $T_p$ denotes the prediction window (PW) size. The source allocates energy to transmit some bits proactively for each time slot of the PW with the objective of reducing the transmission energy over the non-predictive case. The requests are predicted based on the user location utilizing the prior statistics about the user requests at each location. We also assume that the prediction is not perfect. We propose proactive scheduling policies to minimize the expected energy consumption required to transmit the requested packets under two different assumptions on the channel state information at the source. In the first scenario, offline scheduling, we assume the channel states are known a-priori at the source at the beginning of the PW. In the second scenario, online scheduling, it is assumed that the source has causal knowledge of the channel state. Numerical results are presented showing the gains achieved by using proactive scheduling policies compared with classical (reactive) networks. Simulation results also show that increasing the PW size leads to a significant reduction in the consumed transmission energy even with imperfect prediction.
Performance-driven facial animation

As computer graphics technique rises to the challenge of rendering lifelike performers, more lifelike performance is required. The techniques used to animate robots, arthropods, and suits of armor, have been extended to flexible surfaces of fur and flesh. Physical models of muscle and skin have been devised. But more complex databases and sophisticated physical modeling do not directly address the performance problem. The gestures and expressions of a human actor are not the solution to a dynamic system. This paper describes a means of acquiring the expressions of real faces, and applying them to computer-generated faces. Such an "electronic mask" offers a means for the traditional talents of actors to be flexibly incorporated in digital animations. Efforts in a similar spirit have resulted in servo-controlled "animatrons," hightechnology puppets, and CG puppetry . The manner in which the skills of actors and puppetteers as well as animators are accommodated in such systems may point the way for a more general incorporation of human nuance into our emerging computer media.The ensuing description is divided into two major subjects: the construction of a highly-resolved human head model with photographic texture mapping, and the concept demonstration of a system to animate this model by tracking and applying the expressions of a human performer
Contemporary Peer Review in Action: Lessons from Open Source Development

Do you use software peer reviews? Are you happy with your current code review practices? Even though formal inspection is recognized as one of the most effective ways to improve software quality, many software organizations struggle to effectively implement a formal inspection regime. Open source projects use an agile peer review process-based on asynchronous, frequent, incremental reviews that are carried out by invested codevelopers-that contrasts with heavyweight inspection processes. The authors describe lessons from the OSS process that transfer to proprietary software development. They also present a selection of popular tools that support lightweight, collaborative, code review processes and nonintrusive metric collection.
Molecular Nutrition Research—The Modern Way Of Performing Nutritional Science

In spite of amazing progress in food supply and nutritional science, and a striking increase in life expectancy of approximately 2.5 months per year in many countries during the previous 150 years, modern nutritional research has a great potential of still contributing to improved health for future generations, granted that the revolutions in molecular and systems technologies are applied to nutritional questions. Descriptive and mechanistic studies using state of the art epidemiology, food intake registration, genomics with single nucleotide polymorphisms (SNPs) and epigenomics, transcriptomics, proteomics, metabolomics, advanced biostatistics, imaging, calorimetry, cell biology, challenge tests (meals, exercise, etc.), and integration of all data by systems biology, will provide insight on a much higher level than today in a field we may name molecular nutrition research. To take advantage of all the new technologies scientists should develop international collaboration and gather data in large open access databases like the suggested Nutritional Phenotype database (dbNP). This collaboration will promote standardization of procedures (SOP), and provide a possibility to use collected data in future research projects. The ultimate goals of future nutritional research are to understand the detailed mechanisms of action for how nutrients/foods interact with the body and thereby enhance health and treat diet-related diseases.
3D printed dielectric Fresnel lens

This paper presents the design and fabrication of a zone plate Fresnel lens. 3D Printing is used for rapid prototyping this low-cost and light-weight lens to operate at 10 GHz. This lens is comprised of four different 3D printed dielectric zones to form phase compensation in a Fresnel lens. The dielectric zones are fabricated with different infill percentage to create tailored dielectric constants. The dielectric lens offers 18 dBi directivity at 10 GHz when illuminated by a waveguide source.
An Improved Maximum Power Point Tracking for Photovoltaic Grid-Connected Inverter Based on Voltage-Oriented Control

In this paper, an improved maximum power point (MPP) tracking (MPPT) with better performance based on voltage-oriented control (VOC) is proposed to solve a fast-changing irradiation problem. In VOC, a cascaded control structure with an outer dc link voltage control loop and an inner current control loop is used. The currents are controlled in a synchronous orthogonal d,q frame using a decoupled feedback control. The reference current of proportional-integral (PI) d-axis controller is extracted from the dc-side voltage regulator by applying the energy-balancing control. Furthermore, in order to achieve a unity power factor, the q-axis reference is set to zero. The MPPT controller is applied to the reference of the outer loop control dc voltage photovoltaic (PV). Without PV array power measurement, the proposed MPPT identifies the correct direction of the MPP by processing the d-axis current reflecting the power grid side and the signal error of the PI outer loop designed to only represent the change in power due to the changing atmospheric conditions. The robust tracking capability under rapidly increasing and decreasing irradiance is verified experimentally with a PV array emulator. Simulations and experimental results demonstrate that the proposed method provides effective, fast, and perfect tracking.
Computer-Aided Diagnosis of Thyroid Nodule: A Review

Diagnostic imaging is an important tool in medical science due to the constrained observations of the expert and uncertainties in medical knowledge. A thyroid ultrasound is a non-invasive imaging study used to detect and classify abnormalities of the thyroid gland. Computerized system is a valuable and beneficial means for feature extraction and classification of thyroid nodule in order to eliminate operator dependency and to improve the diagnostic accuracy. The aim of this paper is to review existing approaches to the automatic classification of nodules in thyroid ultrasound images, highlighting the keypoints and main differences between the used strategies. The aim of this paper is to review existing approaches for the diagnosis of Nodules in thyroid ultrasound images with their performance measures.
Development of a Student Attendance Management System Using RFID and Face Recognition : A Review

Whole world and administrators of Educational institutions’ in our country are concerned about regularity of student attendance. Student’s overall academic performance is affected by the student’s present in his institute. Mainly there are two conventional methods for attendance taking and they are by calling student nams or by taking student sign on paper. They both were more time consuming and inefficient. Hence, there is a requirement of computer-based student attendance management system which will assist the faculty for maintaining attendance of presence. The paper reviews various computerized attendance management system. In this paper basic problem of student attendance management is defined which is traditionally taken manually by faculty. One alternative to make student attendance system automatic is provided by Computer Vision. In this paper we review the various computerized system which is being developed by using different techniques. Based on this review a new approach for student attendance recording and management is proposed to be used for various colleges or academic institutes.
The Average Laboratory Samples a Population of 7 , 300 Amazon Mechanical Turk Workers

Using capture-recapture analysis we estimate the effective size of the active Amazon Mechanical Turk (MTurk) population that a typical laboratory can access to be about 7,300 workers. We also estimate that the time taken for half of the workers to leave the MTurk pool and be replaced is about 7 months. Each laboratory has its own population pool which overlaps, often extensively, with the hundreds of other laboratories using MTurk. Our estimate is based on a sample of 114,460 completed sessions from 33,408 unique participants and 689 sessions across seven laboratories in the US, Europe, and Australia from January 2012 to March 2015.
Development of computational thinking and collaborative learning in kindergarten using programmable educational robots: a teacher training experience

Nowadays, the teaching-learning processes are constantly changing, one of the latest modifications promises to strengthen the development of digital skills and thinking in the participants, from an early age. In this sense, the present article shows the advances of a study oriented to the formation of programming abilities, computational thinking and collaborative learning in an initial education context. As part of the study it was initially proposed to conduct a training day for teachers who will participate in the experimental phase of the research, considering this human resource as a link of great importance to achieve maximum use of students in the development of curricular themes of the level, using ICT resources and programmable educational robots. The criterion and the positive acceptance expressed by the teaching group after the evaluation applied at the end of the session, constitute a good starting point for the development of the following activities that make up the research in progress.
Secondhand smoke exposure among never-smoking youth in 168 countries.

To estimate the prevalence of secondhand smoke (SHS) exposure among never-smoking adolescents and identify key factors associated with such exposure.



Data were obtained from nationally representative Global Youth Tobacco Surveys conducted in 168 countries during 1999-2008. SHS exposure was ascertained in relation to the location-exposure inside home, outside home, and both inside and outside home, respectively. Independent variables included parental and/or peer smoking, knowledge about smoke harm, attitudes toward smoking ban, age, sex, and World Health Organization region. Simple and multiple logistic regression analyses were conducted.



Of 356,414 never-smoking adolescents included in the study, 30.4%, 44.2%, and 23.2% were exposed to SHS inside home, outside home, and both, respectively. Parental smoking, peer smoking, knowledge about smoke harm, and positive attitudes toward smoke ban were significantly associated with increased odds of SHS exposure. Approximately 14% of adolescents had both smoking parents and peers. Compared with never-smoking adolescents who did not have both smoking parents and peers, those who had both smoking parents and peers had 19 (adjusted odds ratio [aOR], 19.0; 95% confidence interval [CI], 16.86-21.41), eight (aOR, 7.71; 95% CI, 7.05-8.43), and 23 times (aOR, 23.16; 95% CI, 20.74-25.87) higher odds of exposure to SHS inside, outside, and both inside and outcome home, respectively.



Approximately one third and two fifths of never-smoking adolescents were exposed to SHS inside or outside home, and smoking parents and/or peers are the key factors. Study findings highlight the need to develop and implement comprehensive smoke-free policies consistent with the World Health Organization Framework Convention on Tobacco Control.
Densely Connected High Order Residual Network for Single Frame Image Super Resolution

Deep convolutional neural networks (DCNN) have been widely adopted for research on super resolution recently, however previous work focused mainly on stacking as many layers as possible in their model, in this paper, we present a new perspective regarding to image restoration problems that we can construct the neural network model reflecting the physical significance of the image restoration process, that is, embedding the a priori knowledge of image restoration directly into the structure of our neural network model, we employed a symmetric non-linear colorspace, the sigmoidal transfer, to replace traditional transfers such as, sRGB, Rec.709, which are asymmetric non-linear colorspaces, we also propose a “reuse plus patch” method to deal with super resolution of different scaling factors, our proposed methods and model show generally superior performance over previous work even though our model was only roughly trained and could still be underfitting the training set.
Marching cubes: A high resolution 3D surface construction algorithm

We present a new algorithm, called marching cubes, that creates triangle models of constant density surfaces from 3D medical data. Using a divide-and-conquer approach to generate inter-slice connectivity, we create a case table that defines triangle topology. The algorithm processes the 3D medical data in scan-line order and calculates triangle vertices using linear interpolation. We find the gradient of the original data, normalize it, and use it as a basis for shading the models. The detail in images produced from the generated surface models is the result of maintaining the inter-slice connectivity, surface data, and gradient information present in the original 3D data. Results from computed tomography (CT), magnetic resonance (MR), and single-photon emission computed tomography (SPECT) illustrate the quality and functionality of marching cubes. We also discuss improvements that decrease processing time and add solid modeling capabilities.
Recursive symmetries for geometrically complex and materially heterogeneous additive manufacturing

We present a generative method for the creation of geometrically complex andmaterially heterogeneous objects. By combining generative design and additive manufacturing, we demonstrate a unique formfinding approach and method for multi-material 3D printing. The method offers a fast, automated and controllable way to explore an expressive set of symmetrical, complex and colored objects, which makes it a useful tool for design exploration andprototyping.Wedescribe a recursive grammar for the generation of solid boundary surfacemodels suitable for a variety of design domains.We demonstrate the generation and digital fabrication ofwatertight 2-manifold polygonalmeshes, with feature-aligned topology that can be produced on a wide variety of 3D printers, as well as post-processed with traditional 3D modeling tools. To date, objects with intricate spatial patterns and complex heterogeneous material compositions generated by this method can only be produced through 3D printing.
Incremental anomaly-based intrusion detection system using limited labeled data

With the proliferation of the internet and increased global access to online media, cybercrime is also occurring at an increasing rate. Currently, both personal users and companies are vulnerable to cybercrime. A number of tools including firewalls and Intrusion Detection Systems (IDS) can be used as defense mechanisms. A firewall acts as a checkpoint which allows packets to pass through according to predetermined conditions. In extreme cases, it may even disconnect all network traffic. An IDS, on the other hand, automates the monitoring process in computer networks. The streaming nature of data in computer networks poses a significant challenge in building IDS. In this paper, a method is proposed to overcome this problem by performing online classification on datasets. In doing so, an incremental naive Bayesian classifier is employed. Furthermore, active learning enables solving the problem using a small set of labeled data points which are often very expensive to acquire. The proposed method includes two groups of actions i.e. offline and online. The former involves data preprocessing while the latter introduces the NADAL online method. The proposed method is compared to the incremental naive Bayesian classifier using the NSL-KDD standard dataset. There are three advantages with the proposed method: (1) overcoming the streaming data challenge; (2) reducing the high cost associated with instance labeling; and (3) improved accuracy and Kappa compared to the incremental naive Bayesian approach. Thus, the method is well-suited to IDS applications.
Hybrid Beamforming for Massive MIMO: A Survey

Hybrid multiple-antenna transceivers, which combine large-dimensional analog pre/postprocessing with lower-dimensional digital processing, are the most promising approach for reducing the hardware cost and training overhead in massive MIMO systems. This article provides a comprehensive survey of the various incarnations of such structures that have been proposed in the literature. We provide a taxonomy in terms of the required channel state information, that is, whether the processing adapts to the instantaneous or average (second-order) channel state information; while the former provides somewhat better signal- to-noise and interference ratio, the latter has much lower overhead for CSI acquisition. We furthermore distinguish hardware structures of different complexities. Finally, we point out the special design aspects for operation at millimeter-wave frequencies.