nils-herrmann/clean_scirepeval huggingface dataset:
Gamification in assessment: Do points affect test performance?

Gamification, applying game mechanics to nongame contexts, has recently become a hot topic across a wide range of industries, and has been presented as a potential disruptive force in education. It is based on the premise that it can promote motivation and engagement and thus contribute to the learning process. However, research examining this assumption is scarce. In a set of studies we examined the effects of points, a basic element of gamification, on performance in a computerized assessment of mastery and fluency of basic mathematics concepts. The first study, with adult participants, found no effect of the point manipulation on accuracy of responses, although the speed of responses increased. In a second study, with 6e8 grade middle school participants, we found the same results for the two aspects of performance. In addition, middle school participants' reactions to the test revealed higher likeability ratings for the test under the points condition, but only in the first of the two sessions, and perceived effort during the test was higher in the points condition, but only for eighth grade students.
Solutions to Detect and Analyze Online Radicalization : A Survey

Online Radicalization (also called Cyber-Terrorism or Extremism or Cyber-Racism or CyberHate) is widespread and has become a major and growing concern to the society, governments and law enforcement agencies around the world. Research shows that various platforms on the Internet (low barrier to publish content, allows anonymity, provides exposure to millions of users and a potential of a very quick and widespread diffusion of message) such as YouTube (a popular video sharing website), Twitter (an online micro-blogging service), Facebook (a popular social networking website), online discussion forums and blogosphere are being misused for malicious intent. Such platforms are being used to form hate groups, racist communities, spread extremist agenda, incite anger or violence, promote radicalization, recruit members and create virtual organizations and communities. Automatic detection of online radicalization is a technically challenging problem because of the vast amount of the data, unstructured and noisy user-generated content, dynamically changing content and adversary behavior. There are several solutions proposed in the literature aiming to combat and counter cyber-hate and cyber-extremism. In this survey, we review solutions to detect and analyze online radicalization. We review 40 papers published at 12 venues from June 2003 to November 2011. We present a novel classification scheme to classify these papers. We analyze these techniques, perform trend analysis, discuss limitations of existing techniques and find out research gaps.
Mechanical Stress Reduction of Rotor Core of Interior Permanent Magnet Synchronous Motor

In this paper, the bridge shape of interior permanent magnet synchronous motor (IPMSM) is designed for integrated starter and generator (ISG) which is applied in hybrid electric vehicle (HEV). Mechanical stress of rotor core which is caused by centrifugal force is the main issue when IPMSM is operated at high speed. The bridge is thin area in rotor core where is mechanically weak point and the shape of bridge significantly affects leakage flux and electromagnetic performance. Therefore, bridge should be designed considering both mechanic and electromagnetic characteristics. In the design process, we firstly find a shape of bridge has low leakage flux and mechanical stress. Next, the calculation of mechanical stress and the electromagnetic characteristics are performed by finite element analysis (FEA). The mechanical stress in rotor core is not maximized in steady high speed but dynamical high momentum. Therefore, transient FEA is necessary to consider the dynamic speed changing in real speed profile for durability experiment. Before the verification test, fatigue characteristic is investigated by using S-N curve of rotor core material. Lastly, the burst test of rotor is performed and the deformation of rotor core is compared between prototype and designed model to verify the design method.
Cross-lingual Transfer for Unsupervised Dependency Parsing Without Parallel Data

Cross-lingual transfer has been shown to produce good results for dependency parsing of resource-poor languages. Although this avoids the need for a target language treebank, most approaches have still used large parallel corpora. However, parallel data is scarce for low-resource languages, and we report a new method that does not need parallel data. Our method learns syntactic word embeddings that generalise over the syntactic contexts of a bilingual vocabulary, and incorporates these into a neural network parser. We show empirical improvements over a baseline delexicalised parser on both the CoNLL and Universal Dependency Treebank datasets. We analyse the importance of the source languages, and show that combining multiple source-languages leads to a substantial improvement.
Trajectory Planning Design Equations and Control of a 4 - axes Stationary Robotic Arm

This paper features the trajectory planning design of a indigenously developed 4-Axis SCARA robot which is used for doing successful robotic manipulation task in the laboratory. Once, a trajectory is being designed and given as input to the robot, the robot’s gripper tip moves along that specified trajectory. Trajectories have to be designed in the work space only. The main idea of this paper is to design a continuous path trajectory model for the indigenously developed SCARA robot arm during its maneuvering from one point to another point (during pick and place operations) in a workspace avoiding all the obstacles in its path of motion.
A four channel 24-GHz FMCW radar sensor with two-dimensional target localization capabilities

The measurement of different target parameters using radar systems has been an active research area for the last decades. Particularly target angle measurement is a very demanding topic, because obtaining good measurement results often goes hand in hand with extensive hardware effort. Especially for sensors used in the mass market, e.g. in automotive applications like adaptive cruise control this may be prohibitive. Therefore we address target localization using a compact frequency-modulated continuous-wave (FMCW) radar sensor. The angular measurement results are improved compared to standard beamforming methods using an adaptive beamforming approach. This approach will be applied to the FMCW principle in a way that allows the use of well known methods for the determination of other target parameters like range or velocity. The applicability of the developed theory will be shown on different measurement scenarios using a 24-GHz prototype radar system.
A Formalism for C

In this paper, we develop a general formalism for describing the C++ programming language, and regular enough to cope with proposed extensions (such as concepts) for C++0x that affect its type system. Concepts are a mechanism for checking template arguments currently being developed to help cope with the massive use of templates in modern C++. The main challenges in developing a formalism for C++ are scoping, overriding, overloading, templates, specialization, and the C heritage exposed in the built-in types. Here, we primarily focus on templates and overloading.
Dynamic parallax occlusion mapping with approximate soft shadows

This paper presents a per-pixel ray tracing algorithm with dynamic lighting of surfaces in real-time on the GPU. First, we propose a method for increased precision of the critical ray-height field intersection and adaptive height field sampling. We achieve higher quality results than the existing inverse displacement mapping algorithms. Second, soft shadows are computed by estimating light visibility for the displaced surfaces. Third, we describe an adaptive level-of-detail system which uses the information supplied by the graphics hardware during rendering to automatically manage shader complexity. This LOD scheme maintains smooth transitions between the full displacement computation and a simplified representation at a lower level of detail without visual artifacts. The algorithm performs well for animated objects and supports dynamic rendering of height fields for a variety of interesting displacement effects. The presented method is scalable for a range of consumer grade GPU products. It exhibits a low memory footprint and can be easily integrated into existing art pipelines for games and effects rendering.
15 GHz 25 dBm multigate-cell stacked CMOS power amplifier with 32 % PAE and ≥ 30 dB gain for 5G applications

A three-stage stacked FET CMOS power amplifier (PA) for the 12.7 to 15.3 GHz frequency range is presented. The PA achieves more than 30 dB linear gain with saturated output power of 25.1 dBm (320 mW) and peak power added efficiency (PAE) of 32.4 % at 13.5 GHz. The PA is implemented in 45 nm CMOS SOI technology. High gain is achieved with two cascode pre-driver stages and a final high power stage. The output stage comprises 512 four-stack multigate-cell devices to allow high voltage swings and correspondingly high output power. The effective gate width of the output device is 614 μm. To the authors knowledge, combination of power and efficiency achieved in this work are the highest reported for CMOS PAs in the 15 GHz band. The amplifier occupies an area of 1 × 1 mm2 including pads.
Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields

Automatic segmentation of the liver and its lesion is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This paper presents a method to automatically segment liver and lesions in CT abdomen images using cascaded fully convolutional neural networks (CFCNs) and dense 3D conditional random fields (CRFs). We train and cascade two FCNs for a combined segmentation of the liver and its lesions. In the first step, we train a FCN to segment the liver as ROI input for a second FCN. The second FCN solely segments lesions from the predicted liver ROIs of step 1. We refine the segmentations of the CFCN using a dense 3D CRF that accounts for both spatial coherence and appearance. CFCN models were trained in a 2-fold cross-validation on the abdominal CT dataset 3DIRCAD comprising 15 hepatic tumor volumes. Our results show that CFCN-based semantic liver and lesion segmentation achieves Dice scores over 94% for liver with computation times below 100s per volume. We experimentally demonstrate the robustness of the proposed method as a decision support system with a high accuracy and speed for usage in daily clinical routine.
Mechanical design of a shape memory alloy actuated prosthetic hand.

This paper presents the mechanical design for a new five fingered, twenty degree-of-freedom dexterous hand patterned after human anatomy and actuated by Shape Memory Alloy artificial muscles. Two experimental prototypes of a finger, one fabricated by traditional means and another fabricated by rapid prototyping techniques, are described and used to evaluate the design. An important aspect of the Rapid Prototype technique used here is that this multi-articulated hand will be fabricated in one step, without requiring assembly, while maintaining its desired mobility. The use of Shape Memory Alloy actuators combined with the rapid fabrication of the non-assembly type hand, reduce considerably its weight and fabrication time. Therefore, the focus of this paper is the mechanical design of a dexterous hand that combines Rapid Prototype techniques and smart actuators. The type of robotic hand described in this paper can be utilized for applications requiring low weight, compactness, and dexterity such as prosthetic devices, space and planetary exploration.
A variable physical damping actuator (VPDA) for compliant robotic joints

This paper introduces the development of a semi-active friction based variable physical damping actuator (VPDA) unit. The realization of this unit aims to facilitate the control of compliant robotic joints by providing physical variable damping on demand assisting on the regulation of the oscillations induced by the introduction of compliance. The mechatronics details and the dynamic model of the damper are introduced. The proposed variable damper mechanism is evaluated on a simple 1-DOF compliant joint linked to the ground through a torsion spring. This flexible connection emulates a compliant joint, generating oscillations when the link is perturbed. Preliminary results are presented to show that the unit and the proposed control scheme are capable of replicating simulated relative damping values with good fidelity.
Feature Space Independent Semi-Supervised Domain Adaptation via Kernel Matching

Domain adaptation methods aim to learn a good prediction model in a label-scarce target domain by leveraging labeled patterns from a related source domain where there is a large amount of labeled data. However, in many practical domain adaptation learning scenarios, the feature distribution in the source domain is different from that in the target domain. In the extreme, the two distributions could differ completely when the feature representation of the source domain is totally different from that of the target domain. To address the problems of substantial feature distribution divergence across domains and heterogeneous feature representations of different domains, we propose a novel feature space independent semi-supervised kernel matching method for domain adaptation in this work. Our approach learns a prediction function on the labeled source data while mapping the target data points to similar source data points by matching the target kernel matrix to a submatrix of the source kernel matrix based on a Hilbert Schmidt Independence Criterion. We formulate this simultaneous learning and mapping process as a non-convex integer optimization problem and present a local minimization procedure for its relaxed continuous form. We evaluate the proposed kernel matching method using both cross domain sentiment classification tasks of Amazon product reviews and cross language text classification tasks of Reuters multilingual newswire stories. Our empirical results demonstrate that the proposed kernel matching method consistently and significantly outperforms comparison methods on both cross domain classification problems with homogeneous feature spaces and cross domain classification problems with heterogeneous feature spaces.
Multi-Agent Reinforcement Learning: Independent versus Cooperative Agents

Intelligent human agents exist in a coop erative social environment that facilitates learning They learn not only by trial and error but also through cooperation by sharing instantaneous information episodic experience and learned knowledge The key investigations of this paper are Given the same number of reinforcement learning agents will cooperative agents outperform independent agents who do not communicate during learning and What is the price for such cooperation Using independent agents as a benchmark cooperative agents are studied in following ways sharing sensation sharing episodes and shar ing learned policies This paper shows that a additional sensation from another agent is bene cial if it can be used e ciently b shar ing learned policies or episodes among agents speeds up learning at the cost of communica tion and c for joint tasks agents engaging in partnership can signi cantly outperform independent agents although they may learn slowly in the beginning These tradeo s are not just limited to multi agent reinforcement learning
A Multiagent Approach to Autonomous Intersection Management

Artificial intelligence research is ushering in a new era of sophisticated, mass-market transportation technology. While computers can already fly a passenger jet better than a trained human pilot, people are still faced with the dangerous yet tedious task of driving automobiles. Intelligent Transportation Systems (ITS) is the field that focuses on integrating information technology with vehicles and transportation infrastructure to make transportation safer, cheaper, and more efficient. Recent advances in ITS point to a future in which vehicles themselves handle the vast majority of the driving task. Once autonomous vehicles become popular, autonomous interactions amongst multiple vehicles will be possible. Current methods of vehicle coordination, which are all designed to work with human drivers, will be outdated. The bottleneck for roadway efficiency will no longer be the drivers, but rather the mechanism by which those drivers’ actions are coordinated. While open-road driving is a well-studied and more-or-less-solved problem, urban traffic scenarios, especially intersections, are much more challenging. We believe current methods for controlling traffic, specifically at intersections, will not be able to take advantage of the increased sensitivity and precision of autonomous vehicles as compared to human drivers. In this article, we suggest an alternative mechanism for coordinating the movement of autonomous vehicles through intersections. Drivers and intersections in this mechanism are treated as autonomous agents in a multiagent system. In this multiagent system, intersections use a new reservation-based approach built around a detailed communication protocol, which we also present. We demonstrate in simulation that our new mechanism has the potential to significantly outperform current intersection control technology—traffic lights and stop signs. Because our mechanism can emulate a traffic light or stop sign, it subsumes the most popular current methods of intersection control. This article also presents two extensions to the mechanism. The first extension allows the system to control human-driven vehicles in addition to autonomous vehicles. The second gives priority to emergency vehicles without significant cost to civilian vehicles. The mechanism, including both extensions, is implemented and tested in simulation, and we present experimental results that strongly attest to the efficacy of this approach.
Languages as libraries

Programming language design benefits from constructs for extending the syntax and semantics of a host language. While C's string-based macros empower programmers to introduce notational shorthands, the parser-level macros of Lisp encourage experimentation with domain-specific languages. The Scheme programming language improves on Lisp with macros that respect lexical scope.
 The design of Racket---a descendant of Scheme---goes even further with the introduction of a full-fledged interface to the static semantics of the language. A Racket extension programmer can thus add constructs that are indistinguishable from "native" notation, large and complex embedded domain-specific languages, and even optimizing transformations for the compiler backend. This power to experiment with language design has been used to create a series of sub-languages for programming with first-class classes and modules, numerous languages for implementing the Racket system, and the creation of a complete and fully integrated typed sister language to Racket's untyped base language.
 This paper explains Racket's language extension API via an implementation of a small typed sister language. The new language provides a rich type system that accommodates the idioms of untyped Racket. Furthermore, modules in this typed language can safely exchange values with untyped modules. Last but not least, the implementation includes a type-based optimizer that achieves promising speedups. Although these extensions are complex, their Racket implementation is just a library, like any other library, requiring no changes to the Racket implementation.
Road-Condition Recognition Using 24-GHz Automotive Radar

This paper studies 24-GHz automotive radar technology for detecting low-friction spots caused by water, ice, or snow on asphalt. The backscattering properties of asphalt in different conditions are studied in both laboratory and field experiments. In addition, the effect of water on the backscattering properties of asphalt is studied with a surface scattering model. The results suggest that low-friction spots could be detected with a radar by comparing backscattered signals at different polarizations. The requirements for the radar are considered, and a 24-GHz radar for road-condition recognition is found to be feasible.
Iron and Magnet Losses and Torque Calculation of Interior Permanent Magnet Synchronous Machines Using Magnetic Equivalent Circuit

We present a faster and simpler approach for the calculation of iron and magnet losses and torque of an interior permanent-magnet synchronous machine (IPMSM) than finite-element methods (FEM). It uses a magnetic equivalent circuit (MEC) based on large elements and takes into account magnetic saturation and magnet eddy currents. The machine is represented by nonlinear and constant reluctance elements and flux sources. Solution of the nonlinear magnetic circuit is obtained by an iterative method. The results allow the calculation of losses and torque of the machine. Due to the approximations used in the formulation of the MEC, this method is less accurate but faster than nonlinear transient magnetic FEM, and is more useful for the comparison of different machine designs during design optimization.
Autoencoder for words

This paper presents a training method that encodes each word into a different vector in semantic space and its relation to low entropy coding. Elman network is employed in the method to process word sequences from literary works. The trained codes possess reduced entropy and are used in ranking, indexing, and categorizing literary works. A modification of the method to train the multi-vector for each polysemous word is also presented where each vector represents a different meaning of its word. These multiple vectors can accommodate several different meanings of their word. This method is applied to the stylish analyses of two Chinese novels, Dream of the Red Chamber and Romance of the Three Kingdoms. & 2014 Elsevier B.V. All rights reserved.
Gesture spotting with body-worn inertial sensors to detect user activities

We present a method for spotting sporadically occurring gestures in a continuous data stream from body-worn inertial sensors. Our method is based on a natural partitioning of continuous sensor signals and uses a two-stage approach for the spotting task. In a first stage, signal sections likely to contain specific motion events are preselected using a simple similarity search. Those preselected sections are then further classified in a second stage, exploiting the recognition capabilities of hidden Markov models. Based on two case studies, we discuss implementation details of our approach and show that it is a feasible strategy for the spotting of various types of motion events.
On Privacy and Security Challenges in Smart Connected Homes

Smart homes have become increasingly popular for IoT products and services with a lot of promises for improving the quality of life of individuals. Nevertheless, the heterogeneous, dynamic, and Internet-connected nature of this environment adds new concerns as private data becomes accessible, often without the householders' awareness. This accessibility alongside with the rising risks of data security and privacy breaches, makes smart home security a critical topic that deserves scrutiny. In this paper, we present an overview of the privacy and security challenges directed towards the smart home domain. We also identify constraints, evaluate solutions, and discuss a number of challenges and research issues where further investigation is required.