nils-herrmann/clean_scirepeval huggingface dataset:
Representing Local Structure Using Tensors

The fundamental problem of finding a suitable representation of the orientation of 3D surfaces is considered. A representation is regarded suitable if it meets three basic requirements: Uniqueness, Uniformity and Polar separability. A suitable tensor representation is given. At the heart of the problem lies the fact that orientation can only be defined mod 180◦ , i.e the fact that a 180◦ rotation of a line or a plane amounts to no change at all. For this reason representing a plane using its normal vector leads to ambiguity and such a representation is consequently not suitable. The ambiguity can be eliminated by establishing a mapping between R3 and a higherdimensional tensor space. The uniqueness requirement implies a mapping that map all pairs of 3D vectors x and -x onto the same tensor T. Uniformity implies that the mapping implicitly carries a definition of distance between 3D planes (and lines) that is rotation invariant and monotone with the angle between the planes. Polar separability means that the norm of the representing tensor T is rotation invariant. One way to describe the mapping is that it maps a 3D sphere into 6D in such a way that the surface is uniformly stretched and all pairs of antipodal points maps onto the same tensor. It is demonstrated that the above mapping can be realized by sampling the 3D space using a specified class of symmetrically distributed quadrature filters. It is shown that 6 quadrature filters are necessary to realize the desired mapping, the orientations of the filters given by lines trough the vertices of an icosahedron. The desired tensor representation can be obtained by simply performing a weighted summation of the quadrature filter outputs. This situation is indeed satisfying as it implies a simple implementation of the theory and that requirements on computational capacity can be kept within reasonable limits. Noisy neigborhoods and/or linear combinations of tensors produced by the mapping will in general result in a tensor that has no direct counterpart in R3. In an adaptive hierarchical signal processing system, where information is flowing both up (increasing the level of abstraction) and down (for adaptivity and guidance), it is necessary that a meaningful inverse exists for each levelaltering operation. It is shown that the point in R3 that corresponds to the best approximation of a given tensor is given by the largest eigenvalue times the corresponding eigenvector of the tensor.
Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models.

Hypotheses involving mediation are common in the behavioral sciences. Mediation exists when a predictor affects a dependent variable indirectly through at least one intervening variable, or mediator. Methods to assess mediation involving multiple simultaneous mediators have received little attention in the methodological literature despite a clear need. We provide an overview of simple and multiple mediation and explore three approaches that can be used to investigate indirect processes, as well as methods for contrasting two or more mediators within a single model. We present an illustrative example, assessing and contrasting potential mediators of the relationship between the helpfulness of socialization agents and job satisfaction. We also provide SAS and SPSS macros, as well as Mplus and LISREL syntax, to facilitate the use of these methods in applications.
Positive and Unlabeled Learning for Graph Classification

The problem of graph classification has drawn much attention in the last decade. Conventional approaches on graph classification focus on mining discriminative sub graph features under supervised settings. The feature selection strategies strictly follow the assumption that both positive and negative graphs exist. However, in many real-world applications, the negative graph examples are not available. In this paper we study the problem of how to select useful sub graph features and perform graph classification based upon only positive and unlabeled graphs. This problem is challenging and different from previous works on PU learning, because there are no predefined features in graph data. Moreover, the sub graph enumeration problem is NP-hard. We need to identify a subset of unlabeled graphs that are most likely to be negative graphs. However, the negative graph selection problem and the sub graph feature selection problem are correlated. Before the reliable negative graphs can be resolved, we need to have a set of useful sub graph features. In order to address this problem, we first derive an evaluation criterion to estimate the dependency between sub graph features and class labels based on a set of estimated negative graphs. In order to build accurate models for the PU learning problem on graph data, we propose an integrated approach to concurrently select the discriminative features and the negative graphs in an iterative manner. Experimental results illustrate the effectiveness and efficiency of the proposed method.
Persuasive system design: state of the art and future directions

This paper provides an overview of the current state of the art in persuasive systems design. All peer-reviewed full papers published at the first three International Conferences on Persuasive Technology were analyzed employing a literature review framework. Results from this analysis are discussed and directions for future research are suggested. Most research papers so far have been experimental. Five out of six of these papers (84.4%) have addressed behavioral change rather than an attitude change. Tailoring, tunneling, reduction and social comparison have been the most studied methods for persuasion. Quite, surprisingly ethical considerations have remained largely unaddressed in these papers. In general, many of the research papers seem to describe the investigated persuasive systems in a relatively vague manner leaving room for some improvement.
The Canny Edge Detector Revisited

Canny ( 1986) suggested that an optimal edge detector should maximize both signal-to-noise ratio and localization, and he derived mathematical expressions for these criteria. Based on these criteria, he claimed that the optimal step edge detector was similar to a derivative of a gaussian. However, Canny’s work suffers from two problems. First, his derivation of localization criterion is incorrect. Here we provide a more accurate localization criterion and derive the optimal detector from it. Second, and more seriously, the Canny criteria yield an infinitely wide optimal edge detector. The width of the optimal detector can however be limited by considering the effect of the neighbouring edges in the image. If we do so, we find that the optimal step edge detector, according to the Canny criteria, is the derivative of an ISEF filter, proposed by Shen and Castan (Graph. Models Image Proc. 54:112–133, 1992). In addition, if we also consider detecting blurred (or non-sharp) gaussian edges of different widths, we find that the optimal blurred-edge detector is the above optimal step edge detector convolved with a gaussian. This implies that edge detection must be performed at multiple scales to cover all the blur widths in the image. We derive a simple scale selection procedure for edge detection, and demonstrate it in one and two dimensions.
Conformal Magnetic Composite RFID for Wearable RF and Bio-Monitoring Applications

This paper introduces for the first time a novel flexible magnetic composite material for RF identification (RFID) and wearable RF antennas. First, one conformal RFID tag working at 480 MHz is designed and fabricated as a benchmarking prototype and the miniaturization concept is verified. Then, the impact of the material is thoroughly investigated using a hybrid method involving electromagnetic and statistical tools. Two separate statistical experiments are performed, one for the analysis of the impact of the relative permittivity and permeability of the proposed material and the other for the evaluation of the impact of the dielectric and magnetic loss on the antenna performance. Finally, the effect of the bending of the antenna is investigated, both on the S-parameters and on the radiation pattern. The successful implementation of the flexible magnetic composite material enables the significant miniaturization of RF passives and antennas in UHF frequency bands, especially when conformal modules that can be easily fine-tuned are required in critical biomedical and pharmaceutical applications.
Encumbrance-free telepresence system with real-time 3D capture and display using commodity depth cameras

This paper introduces a proof-of-concept telepresence system that offers fully dynamic, real-time 3D scene capture and continuous-viewpoint, head-tracked stereo 3D display without requiring the user to wear any tracking or viewing apparatus. We present a complete software and hardware framework for implementing the system, which is based on an array of commodity Microsoft Kinect™color-plus-depth cameras. Novel contributions include an algorithm for merging data between multiple depth cameras and techniques for automatic color calibration and preserving stereo quality even with low rendering rates. Also presented is a solution to the problem of interference that occurs between Kinect cameras with overlapping views. Emphasis is placed on a fully GPU-accelerated data processing and rendering pipeline that can apply hole filling, smoothing, data merger, surface generation, and color correction at rates of up to 100 million triangles/sec on a single PC and graphics board. Also presented is a Kinect-based marker-less tracking system that combines 2D eye recognition with depth information to allow head-tracked stereo views to be rendered for a parallax barrier autostereoscopic display. Our system is affordable and reproducible, offering the opportunity to easily deliver 3D telepresence beyond the researcher's lab.
Neural correlates of feeling sympathy

Positron emission tomography (PET) was used to investigate the neural correlates of feeling sympathy for someone else (i.e. the affinity, association, or relationship between persons wherein whatever affects one similarly affects the other). While undergoing PET scans, subjects were presented with a series of video-clips showing individuals (who were semi-professional stage actors) telling sad and neutral stories, as if they had personally experienced them. These stories were told with either congruent or incongruent motor expression of emotion (MEE). At the end of each movie, subjects were asked to rate the mood of the communicator and also how likable they found that person. Watching sad stories versus neutral stories was associated with increased activity in emotion processing-related structures, as well as in a set of cortical areas that belong to a "shared representation" network, including the right inferior parietal cortex. Motor expression of emotion, regardless of the narrative content of the stories, resulted in a specific regional cerebral blood flow (rCBF) increase in the left inferior frontal gyrus. The condition of mismatch between the narrative content of the stories and the motor expression of emotion elicited a significant skin conductance response and strong rCBF increase in the ventromedial prefrontal cortex and superior frontal gyrus which are involved in dealing with social conflict. Taken together, these results are consistent with a model of feeling sympathy that relies on both the shared representation and the affective networks. Interestingly, this network was not activated when subjects watched inappropriate social behavior.
A neural joint model for entity and relation extraction from biomedical text

Extracting biomedical entities and their relations from text has important applications on biomedical research. Previous work primarily utilized feature-based pipeline models to process this task. Many efforts need to be made on feature engineering when feature-based models are employed. Moreover, pipeline models may suffer error propagation and are not able to utilize the interactions between subtasks. Therefore, we propose a neural joint model to extract biomedical entities as well as their relations simultaneously, and it can alleviate the problems above. Our model was evaluated on two tasks, i.e., the task of extracting adverse drug events between drug and disease entities, and the task of extracting resident relations between bacteria and location entities. Compared with the state-of-the-art systems in these tasks, our model improved the F1 scores of the first task by 5.1% in entity recognition and 8.0% in relation extraction, and that of the second task by 9.2% in relation extraction. The proposed model achieves competitive performances with less work on feature engineering. We demonstrate that the model based on neural networks is effective for biomedical entity and relation extraction. In addition, parameter sharing is an alternative method for neural models to jointly process this task. Our work can facilitate the research on biomedical text mining.
Binary PSO with mutation operator for feature selection using decision tree applied to spam detection

In this paper, we proposed a novel spam detection method that focused on reducing the false positive error of mislabeling nonspam as spam. First, we used the wrapper-based feature selection method to extract crucial features. Second, the decision tree was chosen as the classifier model with C4.5 as the training algorithm. Third, the cost matrix was introduced to give different weights to two error types, i.e., the false positive and the false negative errors. We define the weight parameter as a to adjust the relative importance of the two error types. Fourth, K-fold cross validation was employed to reduce out-ofsample error. Finally, the binary PSO with mutation operator (MBPSO) was used as the subset search strategy. Our experimental dataset contains 6000 emails, which were collected during the year of 2012. We conducted a Kolmogorov–Smirnov hypothesis test on the capital-run-length related features and found that all the p values were less than 0.001. Afterwards, we found a = 7 was the most appropriate in our model. Among seven meta-heuristic algorithms, we demonstrated the MBPSO is superior to GA, RSA, PSO, and BPSO in terms of classification performance. The sensitivity, specificity, and accuracy of the decision tree with feature selection by MBPSO were 91.02%, 97.51%, and 94.27%, respectively. We also compared the MBPSO with conventional feature selection methods such as SFS and SBS. The results showed that the MBPSO performs better than SFS and SBS. We also demonstrated that wrappers are more effective than filters with regard to classification performance indexes. It was clearly shown that the proposed method is effective, and it can reduce the false positive error without compromising the sensitivity and accuracy values.
Usability Heuristics for Touchscreen-based Mobile Devices

Usability is a main quality attribute for any interactive product. Usability in touch screen-based mobile devices is something essential and should be considered when launching a new product, it could be a distinguishing feature in a rushing market, as it is the one of the mobile devices nowadays. Traditional methods for usability measuring do not really fit the nature of these devices. There is a need for new usability evaluation methods or at least for the use of traditional evaluations in novel ways. A set of specific usability heuristics for touch screen-based mobile devices is proposed and (preliminary) validated.
Mastering Sketching: Adversarial Augmentation for Structured Prediction

We present an integral framework for training sketch simplification networks that convert challenging rough sketches into clean line drawings. Our approach augments a simplification network with a discriminator network, training both networks jointly so that the discriminator network discerns whether a line drawing is real training data or the output of the simplification network, which, in turn, tries to fool it. This approach has two major advantages: first, because the discriminator network learns the structure in line drawings, it encourages the output sketches of the simplification network to be more similar in appearance to the training sketches. Second, we can also train the networks with additional unsupervised data: by adding rough sketches and line drawings that are not corresponding to each other, we can improve the quality of the sketch simplification. Thanks to a difference in the architecture, our approach has advantages over similar adversarial training approaches in stability of training and the aforementioned ability to utilize unsupervised training data. We show how our framework can be used to train models that significantly outperform the state of the art in the sketch simplification task, despite using the same architecture for inference. We also present an approach to optimize for a single image, which improves accuracy at the cost of additional computation time. Finally, we show that, using the same framework, it is possible to train the network to perform the inverse problem, i.e., convert simple line sketches into pencil drawings, which is not possible using the standard mean squared error loss. We validate our framework with two user tests, in which our approach is preferred to the state of the art in sketch simplification 88.9% of the time.
A literature survey on load – frequency control for conventional and distribution generation power systems

In this paper an extensive literature review on load–frequency control (LFC) problem in power system has been highlighted. The various configuration of power system models and control techniques/ strategies that concerns to LFC issues have been addressed in conventional as well as distribution generation-based power systems. Further, investigations on LFC challenges incorporating storage devices BESS/SMES, FACTS devices, wind–diesel and PV systems etc have been discussed too. & 2013 Elsevier Ltd. All rights reserved.
On preventing ARP poisoning attack utilizing Software Defined Network (SDN) paradigm

In this work, SDN has been utilized to alleviate and eliminate the problem of ARP poisoning attack. This attack is the underlying infrastructure for many other network attacks, such as, man in the middle, denial of service and session hijacking. In this paper we propose a new algorithm to resolve the problem of ARP spoofing. The algorithm can be applied in two different scenarios. The two scenarios are based on whether a network host will be assigned a dynamic or a static IP address. We call the first scenario SDN_DYN; the second scenario is called SDN_STA. For the evaluation process, a physical SDN-enabled switch has been utilized with Ryu controller. Our results show that the new algorithm can prevent ARP spoofing and other attacks exploiting it.
External-Excitation Curl Antenna

A strip curl antenna is investigated for obtaining a circularly-polarized (CP) tilted beam. This curl is excited through a strip line (called the excitation line) that connects the curl arm to a coaxial feed line. The antenna structure has the following features: a small circumference not exceeding two wavelengths and a small antenna height of less than 0.42 wavelength. The antenna arm is printed on a dielectric hollow cylinder, leading to a robust structure. The investigation reveals that an external excitation for the curl using a straight line (ST-line) is more effective for generating a tilted beam than an internal excitation. It is found that the axial ratio of the radiation field from the external-excitation curl is improved by transforming the ST-line into a wound line (WD-line). It is also found that a modification to the end area of the WD-line leads to an almost constant input impedance (50 ohms). Note that these results are demonstrated for the Ku-band (from 11.7 GHz to 12.75 GHz, 8.6% bandwidth).
YouTube live and Twitch: a tour of user-generated live streaming systems

User-Generated live video streaming systems are services that allow anybody to broadcast a video stream over the Internet. These Over-The-Top services have recently gained popularity, in particular with e-sport, and can now be seen as competitors of the traditional cable TV. In this paper, we present a dataset for further works on these systems. This dataset contains data on the two main user-generated live streaming systems: Twitch and the live service of YouTube. We got three months of traces of these services from January to April 2014. Our dataset includes, at every five minutes, the identifier of the online broadcaster, the number of people watching the stream, and various other media information. In this paper, we introduce the dataset and we make a preliminary study to show the size of the dataset and its potentials. We first show that both systems generate a significant traffic with frequent peaks at more than 1 Tbps. Thanks to more than a million unique uploaders, Twitch is in particular able to offer a rich service at anytime. Our second main observation is that the popularity of these channels is more heterogeneous than what have been observed in other services gathering user-generated content.
Music lessons enhance IQ.

The idea that music makes you smarter has received considerable attention from scholars and the media. The present report is the first to test this hypothesis directly with random assignment of a large sample of children (N = 144) to two different types of music lessons (keyboard or voice) or to control groups that received drama lessons or no lessons. IQ was measured before and after the lessons. Compared with children in the control groups, children in the music groups exhibited greater increases in full-scale IQ. The effect was relatively small, but it generalized across IQ subtests, index scores, and a standardized measure of academic achievement. Unexpectedly, children in the drama group exhibited substantial pre- to post-test improvements in adaptive social behavior that were not evident in the music groups.
Highly Loaded Evanescent Cavities for Widely Tunable High-Q Filters

In the present work, a widely tunable high-Q air filled evanescent cavity bandpass filter is created in an LTCC substrate. A low loss Rogers Duroidreg flexible substrate forms the top of the filter, acting as a membrane for a tunable parasitic capacitor that allows variable frequency loading. A commercially available piezoelectric actuator is mounted on the Duroidreg substrate for precise electrical tuning of the filter center frequency. The filter is tuned from 2.71 to 4.03 GHz, with insertion losses ranging from 1.3 to 2.4 dB across the range for a 2.5% bandwidth filter. Secondarily, an exceptionally narrow band filter is fabricated to show the potential for using the actuators to fine tune the response to compensate for fabrication tolerances. While most traditional machining techniques would not allow for such narrow band filtering, the high-Q and the sensitive tuning combine to allow for near channel selection for a front-end receiver. For further analysis, a widely tunable resonator is also created with a 100% tunable frequency range, from 2.3 to 4.6 GHz. The resonator analysis gives unloaded quality factors ranging from 360 to 700 with a maximum frequency loading of 89%. This technique shows a lot of promise for tunable RF filtering applications.
Improved single image dehazing using dark channel prior

Atmospheric conditions induced by suspended particles, such as fog and haze, severely degrade image quality. Haze removal from a single image of a weather-degraded scene remains a challenging task, because the haze is dependent on the unknown depth information. In this paper, we introduce an improved single image dehazing algorithm which based on the atmospheric scattering physics-based models. We apply the local dark channel prior on selected region to estimate the atmospheric light, and obtain more accurate result. Experiments on real images validate our approach.