nils-herrmann/clean_scirepeval huggingface dataset:
The Impact of Emotion Displays in Embodied Agents on Emergence of Cooperation with People

Acknowledging the social functions of emotion in people, there has been growing interest in the interpersonal effect of emotion on cooperation in social dilemmas. This paper explores whether and how facial displays of emotion in embodied agents impact cooperation with human users. The paper describes an experiment where participants play the iterated prisoner's dilemma against two different agents that play the same strategy (tit-for-tat), but communicate different goal orientations (cooperative vs. individualistic) through their patterns of facial displays. The results show that participants are sensitive to differences in the displays of emotion and cooperate significantly more with the cooperative agent. The results also reveal that cooperation rates are only significantly different when people play first with the individualistic agent. This is in line with the well-known black-hat/white-hat effect from the negotiation literature. However, this study emphasizes that people can discern a cooperator (white-hat) from a noncooperator (black-hat) based only on emotion displays. We propose that people are able to identify the cooperator by inferring, from the emotion displays, the agent's goals. We refer to this as reverse appraisal, as it reverses the usual process in which appraising relevant events with respect to one's goals leads to specific emotion displays. We discuss implications for designing human–computer interfaces and understanding human–human interaction.
Torque Ripple Suppression in Flux-Switching PM Motor by Harmonic Current Injection Based on Voltage Space-Vector Modulation

The flux-switching permanent-magnet (FSPM) machine is a relatively novel brushless machine having magnets and concentrated windings in the stator instead of rotor, which exhibits inherently sinusoidal back-EMF waveform and high torque capability. However, due to the high airgap flux density produced by magnets and the salient poles in both stator and rotor, the resultant torque ripple is relatively large, which is unfavorable for high performance drive system. In this paper, instead of conventional optimization on the machine itself, a new torque ripple suppression approach is proposed in which a series of specific harmonic currents are added into q-axis reference current, resulting in additional torque components to counteract the fundamental and second-order harmonic components of cogging torque. Both the simulation and experimental results confirm that the proposed approach can effectively suppress the torque ripple. It should be emphasized that this method is applicable to all PM machines having relatively large cogging torque.
Learning Social Etiquette: Human Trajectory Understanding In Crowded Scenes

• New large-scale dataset publicly available • Better understanding of a target behaviors • Better prediction and tracking •  Yamaguchi, K., Berg, A.C., Ortiz, L.E., Berg,  Who are you with and where are you going? In: Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on,  (2011) 1345–1352 •  Yamaguchi, K., Berg, A.C., Ortiz, L.E., Berg,  Who are you with and where are you going? In: Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on,  (2011) 1345–1352 •  Trautman, P., Krause, A.: Unfreezing the robot: Navigation in dense, interacting crowds.  Intelligent Robots and Systems (IROS), 2010  (2010) 797–803 Acknowledgments
Toward a more practical unsupervised anomaly detection system

During the last decade, various machine learning and data mining techniques have been applied to Intrusion Detection Systems (IDSs) which have played an important role in defending critical computer systems and networks from cyber attacks. Unsupervised anomaly detection techniques have received a particularly great amount of attention because they enable construction of intrusion detection models without using labeled training data (i.e., with instances preclassified as being or not being an attack) in an automated manner and offer intrinsic ability to detect unknown attacks; i.e., 0-day attacks. Despite the advantages, it is still not easy to deploy them into a real network environment because they require several parameters during their building process, and thus IDS operators and managers suffer from tuning and optimizing the required parameters based on changes of their network characteristics. In this paper, we propose a new anomaly detection method by which we can automatically tune and optimize the values of parameters without predefining them. We evaluated the proposed method over real traffic data obtained from Kyoto University honeypots. The experimental results show that the performance of the proposed method is superior to that of the previous one.
A systematic literature review of empirical evidence on computer games and serious games

This paper examines the literature on computer games and serious games in regard to the potential positive impacts of gaming on users aged 14 years or above, especially with respect to learning, skill enhancement and engagement. Search terms identified 129 papers reporting empirical evidence about the impacts and outcomes of computer games and serious games with respect to learning and engagement and a multidimensional approach to categorizing games was developed. The findings revealed that playing computer games is linked to a range of perceptual, cognitive, behavioural, affective and motivational impacts and outcomes. The most frequently occurring outcomes and impacts were knowledge acquisition/content understanding and affective and motivational outcomes. The range of indicators and measures used in the included papers are discussed, together with methodological limitations and recommendations for further work in this area.
A Technique for Improving P&O MPPT Performances of Double-Stage Grid-Connected Photovoltaic Systems

In double-stage grid-connected photovoltaic (PV) inverters, the dynamic interactions among the DC/DC and DC/AC stages and the maximum power point tracking (MPPT) controller may reduce the system performances. In this paper, the detrimental effects, particularly in terms of system efficiency and MPPT performances, of the oscillations of the PV array voltage, taking place at the second harmonic of the grid frequency are evidenced. The use of a proper compensation network acting on the error signal between a reference signal provided by the MPPT controller and a signal that is proportional to the PV array voltage is proposed. The guidelines for the proper joint design of the compensation network (which is able to cancel out the PV voltage oscillations) and of the main MPPT parameters are provided in this paper. Simulation results and experimental measurements confirm the effectiveness of the proposed approach.
Curriculum and Evaluation Standards for School Mathematics Grades

The need for curricular reform in K-4 mathematics is clear. Such reform must address both the content and emphasis of the curriculum as well as approaches to instruction. A longstanding preoccupation with computation and other traditional skills has dominated both what mathematics is taught and the way mathematics is taught at this level. As a result, the present K-4 curriculum is narrow in scope; fails to foster mathematical insight, reasoning, and problem solving; and emphasizes rote activities. Even more significant is that children begin to lose their belief that learning mathematics is a sense-making experience. They become passive receivers of rules and procedures rather than active participants in creating knowledge.
Off-line Handwriting Text Line Segmentation : A Review

Text line segmentation is an essential pre-processing stage for off-line handwriting recognition in many Optical Character Recognition (OCR) systems. It is an important step because inaccurately segmented text lines will cause errors in the recognition stage. Text line segmentation of the handwritten documents is still one of the most complicated problems in developing a reliable OCR. The nature of handwriting makes the process of text line segmentation very challenging. Several techniques to segment handwriting text line have been proposed in the past. This paper seeks to provide a comprehensive review of the methods of off-line handwriting text line segmentation proposed by researchers.
DC/DC and DC/AC Converters Control for Hybrid Electric Vehicles Energy Management-Ultracapacitors and Fuel Cell

This paper presents the ultracapacitors and the fuel cell (FC) connection for hybrid electric vehicles (HEVs) applications. An original method for the embedded energy management is proposed. This method is used to share the energetic request of the HEV between the ultracapacitors and the FC. The ultracapacitors are linked to dc-bus through the buck-boost converter, and the FC is connected to dc-bus via a boost converter. An asynchronous machine is used like traction motor or generator, and it is connected to dc-bus through an inverter. A dc-motor is used to drive the asynchronous machine during the decelerations and the braking operations. The main contribution of this paper is focused on the embedded energy management based on the new European drive cycle (NEDC), using polynomial control technique. The performances of the proposed control method are evaluated through some simulations and the experimental tests dedicated to HEVs applications.
Generating Consistent Land Surface Temperature and Emissivity Products Between ASTER and MODIS Data for Earth Science Research

Land surface temperature and emissivity (LST&E) products are generated by the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) on the National Aeronautics and Space Administration's Terra satellite. These products are generated at different spatial, spectral, and temporal resolutions, resulting in discrepancies between them that are difficult to quantify, compounded by the fact that different retrieval algorithms are used to produce them. The highest spatial resolution MODIS emissivity product currently produced is from the day/night algorithm, which has a spatial resolution of 5 km. The lack of a high-spatial-resolution emissivity product from MODIS limits the usefulness of the data for a variety of applications and limits utilization with higher resolution products such as those from ASTER. This paper aims to address this problem by using the ASTER Temperature Emissivity Separation (TES) algorithm, combined with an improved atmospheric correction method, to generate the LST&E products for MODIS at 1-km spatial resolution and for ASTER in a consistent manner. The rms differences between the ASTER and MODIS emissivities generated from TES over the southwestern U.S. were 0.013 at 8.6 μm and 0.0096 at 11 μm, with good correlations of up to 0.83. The validation with laboratory-measured sand samples from the Algodones and Kelso Dunes in CA showed a good agreement in spectral shape and magnitude, with mean emissivity differences in all bands of 0.009 and 0.010 for MODIS and ASTER, respectively. These differences are equivalent to approximately 0.6 K in the LST for a material at 300 K and at 11 μm.
Generalized channel inversion methods for multiuser MIMO systems

Block diagonalization (BD) is a well-known precoding method in multiuser multi-input multi-output (MIMO) broadcast channels. This scheme can be considered as a extension of the zero-forcing (ZF) channel inversion to the case where each receiver is equipped with multiple antennas. One of the limitation of the BD is that the sum rate does not grow linearly with the number of users and transmit antennas at low and medium signal-to-noise ratio regime, since the complete suppression of multi-user interference is achieved at the expense of noise enhancement. Also it performs poorly under imperfect channel state information. In this paper, we propose a generalized minimum mean-squared error (MMSE) channel inversion algorithm for users with multiple antennas to overcome the drawbacks of the BD for multiuser MIMO systems. We first introduce a generalized ZF channel inversion algorithm as a new approach of the conventional BD. Applying this idea to the MMSE channel inversion for identifying orthonormal basis vectors of the precoder, and employing the MMSE criterion for finding its combining matrix, the proposed scheme increases the signal-to-interference-plus-noise ratio at each user's receiver. Simulation results confirm that the proposed scheme exhibits a linear growth of the sum rate, as opposed to the BD scheme. For block fading channels with four transmit antennas, the proposed scheme provides a 3 dB gain over the conventional BD scheme at 1% frame error rate. Also, we present a modified precoding method for systems with channel estimation errors and show that the proposed algorithm is robust to channel estimation errors.
New Multilevel Bang-Bang Phase Detector

Equivalent time oscilloscopes are widely used as an alternative to real-time oscilloscopes when high timing resolution is needed. For their correct operation, they need the trigger signal to be accurately aligned to the incoming data, which is achieved by the use of a clock and data recovery circuit (CDR). In this paper, a new multilevel bang-bang phase detector (BBPD) for CDRs is presented; the proposed phase detection scheme disregards samples taken close to the data transitions for the calculation of the phase difference between the inputs, thus eliminating metastability, one of the main issues hindering the performance of BBPDs.
A nonlinear disturbance observer for robotic manipulators

A new nonlinear disturbance observer (NDO) for robotic manipulators is derived in this paper. The global exponential stability of the proposed disturbance observer (DO) is guaranteed by selecting design parameters, which depend on the maximum velocity and physical parameters of robotic manipulators. This new observer overcomes the disadvantages of existing DO’s, which are designed or analyzed by linear system techniques. It can be applied in robotic manipulators for various purposes such as friction compensation, independent joint control, sensorless torque control, and fault diagnosis. The performance of the proposed observer is demonstrated by the friction estimation and compensation for a two-link robotic manipulator. Both simulation and experimental results show the NDO works well.
Leveraging Linux Containers to Achieve High Availability for Cloud Services

In this work, we present a novel approach that leverages Linux containers to achieve High Availability (HA) for cloud applications. A middleware that is comprised of a set of HA agents is defined to compensate the limitations of Linux containers in achieving HA. In our approach we start modeling at the application level, considering the dependencies among application components. We generate the proper scheduling scheme and then deploy the application across containers in the cloud. For each container that hosts critical component(s), we continuously monitor its status and checkpoint its full state, and then react to its failure by restarting locally or failing over to another host where we resume the computing from the most recent state. By using this strategy, all components hosted in a container are preserved without intrusively imposing modification on the application side. Finally, the feasibility of our approach is verified by building a proof-of-concept prototype and a case study of a video streaming application.
Blind dehazing using internal patch recurrence

Images of outdoor scenes are often degraded by haze, fog and other scattering phenomena. In this paper we show how such images can be dehazed using internal patch recurrence. Small image patches tend to repeat abundantly inside a natural image, both within the same scale, as well as across different scales. This behavior has been used as a strong prior for image denoising, super-resolution, image completion and more. Nevertheless, this strong recurrence property significantly diminishes when the imaging conditions are not ideal, as is the case in images taken under bad weather conditions (haze, fog, underwater scattering, etc.). In this paper we show how we can exploit the deviations from the ideal patch recurrence for "Blind De-hazing" — namely, recovering the unknown haze parameters and reconstructing a haze-free image. We seek the haze parameters that, when used for dehazing the input image, will maximize the patch recurrence in the dehazed output image. More specifically, pairs of co-occurring patches at different depths (hence undergoing different degrees of haze) allow recovery of the airlight color, as well as the relative-transmission of each such pair of patches. This in turn leads to dense recovery of the scene structure, and to full image dehazing.
Human pose estimation with global motion cues

We present a novel method to estimate full-body human pose in video sequence by incorporating global motion cues. It has been demonstrated that temporal constraints can largely enhance the pose estimation. Most current approaches typically employ local motion to propagate pose detections to supplement the pose candidates. However, the local motion estimation is often inaccurate under fast movements of body parts and unhelpful when no strong detections achieved in adjacent frames. In this paper, we propose to propagate the detection in each frame using the global motion estimation. Benefiting from the strong detections, our algorithm first produces reasonable trajectory hypotheses for each body part. Then, we cast pose estimation as an optimization problem defined on these trajectories with spatial links between body parts. In the optimization process, we select body part trajectory rather than body part candidate to infer the human pose. Experimental results demonstrate significant performance improvement in comparison with the state-of-the-art methods.
A video database of moving faces and people

We describe a database of static images and video clips of human faces and people that is useful for testing algorithms for face and person recognition, head/eye tracking, and computer graphics modeling of natural human motions. For each person there are nine static "facial mug shots" and a series of video streams. The videos include a "moving facial mug shot," a facial speech clip, one or more dynamic facial expression clips, two gait videos, and a conversation video taken at a moderate distance from the camera. Complete data sets are available for 284 subjects and duplicate data sets, taken subsequent to the original set, are available for 229 subjects.
CharNER: Character-Level Named Entity Recognition

We describe and evaluate a character-level tagger for language-independent Named Entity Recognition (NER). Instead of words, a sentence is represented as a sequence of characters. The model consists of stacked bidirectional LSTMs which inputs characters and outputs tag probabilities for each character. These probabilities are then converted to consistent word level named entity tags using a Viterbi decoder. We are able to achieve close to state-of-the-art NER performance in seven languages with the same basic model using only labeled NER data and no hand-engineered features or other external resources like syntactic taggers or Gazetteers.
A Probabilistic Soft Logic Based Approach to Exploiting Latent and Global Information in Event Classification

Global information such as event-event association, and latent local information such as fine-grained entity types, are crucial to event classification. However, existing methods typically focus on sophisticated local features such as part-ofspeech tags, either fully or partially ignoring the aforementioned information. By contrast, this paper focuses on fully employing them for event classification. We notice that it is difficult to encode some global information such as eventevent association for previous methods. To resolve this problem, we propose a feasible approach which encodes global information in the form of logic using Probabilistic Soft Logic model. Experimental results show that, our proposed approach advances state-of-the-art methods, and achieves the best F1 score to date on the ACE data set.
Feedback on household electricity consumption : a tool for saving energy ?

Improved feedback on electricity consumption may provide a tool for customers to better control their consumption and ultimately save energy. This paper asks which kind of feedback is most successful. For this purpose, a psychological model is presented that illustrates how and why feedback works. Relevant features of feedback are identified that may determine its effectiveness: frequency, duration, content, breakdown, medium and way of presentation, comparisons, and combination with other instruments. The paper continues with an analysis of international experience in order to find empirical evidence for which kinds of feedback work best. In spite of considerable data restraints and research gaps, there is some indication that the most successful feedback combines the following features: it is given frequently and over a long time, provides an appliance-specific breakdown, is presented in a clear and appealing way, and uses computerized and interactive tools.
Trajectory similarity measures

Storing, querying, and analyzing trajectories is becoming increasingly important, as the availability and volumes of trajectory data increases. One important class of trajectory analysis is computing trajectory similarity. This paper introduces and compares four of the most common measures of trajectory similarity: longest common subsequence (LCSS), Fréchet distance, dynamic time warping (DTW), and edit distance. These four measures have been implemented in a new open source R package, freely available on CRAN . The paper highlights some of the differences between these four similarity measures, using real trajectory data, in addition to indicating some of the important emerging applications for measurement of trajectory similarity.
State of the Art Review of Mobile Payment Technology

Mobile payments will gain significant traction in the coming years as the mobile and payment technologies mature and become widely available. Various technologies are competing to become the established standards for physical and virtual mobile payments, yet it is ultimately the users who will determine the level of success of the technologies through their adoption. Only if it becomes easier and cheaper to transact business using mobile payment applications than by using conventional methods will they become popular, either with users or providers. This document is a state of the art review of mobile payment technologies. It covers all of the technologies involved in a mobile payment solution, including mobile networks in section 2, mobile services in section 3, mobile platforms in section 4, mobile commerce in section 5 and different mobile payment solutions in sections 6 to 8.