nils-herrmann/clean_scirepeval huggingface dataset:
Magic Quadrant for Data Quality Tools

Extensive data on functional capabilities, customer base demographics, financial status, pricing and other quantitative attributes gained via a requestfor-information process engaging vendors in this market. Interactive briefings in which vendors provided Gartner with updates on their strategy, market positioning, recent key developments and product road map. A Web-based survey of reference customers provided by each vendor, which captured data on usage patterns, levels of satisfaction with major product functionality categories, various nontechnology-related vendor attributes (such as pricing, product support and overall service delivery), and more. In total, 333 organizations across all major world regions provided input on their experiences with vendors and tools in this manner. Feedback about tools and vendors captured during conversations with users of Gartner's client inquiry service. Market share and revenue growth estimates developed by Gartner's Technology and Service Provider research unit.
Zewen at SemEval-2018 Task 1: An Ensemble Model for Affect Prediction in Tweets

This paper presents a method for Affect in Tweets, which is the task to automatically determine the intensity of emotions and intensity of sentiment of tweets. The term affect refers to emotion-related categories such as anger, fear, etc. Intensity of emotions need to be quantified into a real valued score in [0, 1]. We propose an ensemble system including four different deep learning methods which are CNN, Bidirectional LSTM (BLSTM), LSTMCNN and a CNN-based Attention model (CA). Our system gets an average Pearson correlation score of 0.682 in the subtask EI-reg and an average Pearson correlation score of 0.784 in subtask V-reg, which ranks 19th among 48 systems in EI-reg and 17th among 38 systems in V-reg.
ICT Standardization Strategies and Service Innovation in Health Care

Standards have played an important, but often unrecognized role in the development of modern organizations. This role is accentuated by today's growth of large business and government infrastructures, in the turbulent processes of globalization. In this article we investigate the relationships and tensions – between standardization strategies and service innovation in the health care sector. Our empirical material is nine longitudinal case studies in the Norwegian healthcare sector, collected and analyzed over a period of 20 years. We identify three generic standardization strategies; anticipated standardization, integrated solutions and flexible generification. We argue that the two first strategies do not support service innovation, while the strategy of flexible generification allows for local experimentation, and is also scalable.
The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data

Physiological noise is one of the major confounds for fMRI. A common class of correction methods model noise from peripheral measures, such as ECGs or pneumatic belts. However, physiological noise correction has not emerged as a standard preprocessing step for fMRI data yet due to: (1) the varying data quality of physiological recordings, (2) non-standardized peripheral data formats and (3) the lack of full automatization of processing and modeling physiology, required for large-cohort studies.



We introduce the PhysIO Toolbox for preprocessing of physiological recordings and model-based noise correction. It implements a variety of noise models, such as RETROICOR, respiratory volume per time and heart rate variability responses (RVT/HRV). The toolbox covers all intermediate steps - from flexible read-in of data formats to GLM regressor/contrast creation - without any manual intervention.



We demonstrate the workflow of the toolbox and its functionality for datasets from different vendors, recording devices, field strengths and subject populations. Automatization of physiological noise correction and performance evaluation are reported in a group study (N=35).


COMPARISON WITH EXISTING 
The PhysIO Toolbox reproduces physiological noise patterns and correction efficacy of previously implemented noise models. It increases modeling robustness by outperforming vendor-provided peak detection methods for physiological cycles. Finally, the toolbox offers an integrated framework with full automatization, including performance monitoring, and flexibility with respect to the input data.



Through its platform-independent Matlab implementation, open-source distribution, and modular structure, the PhysIO Toolbox renders physiological noise correction an accessible preprocessing step for fMRI data.
The MegaFace Benchmark: 1 Million Faces for Recognition at Scale

Recent face recognition experiments on a major benchmark (LFW ) show stunning performance-a number of algorithms achieve near to perfect score, surpassing human recognition rates. In this paper, we advocate evaluations at the million scale (LFW includes only 13K photos of 5K people). To this end, we have assembled the MegaFace dataset and created the first MegaFace challenge. Our dataset includes One Million photos that capture more than 690K different individuals. The challenge evaluates performance of algorithms with increasing numbers of "distractors" (going from 10 to 1M) in the gallery set. We present both identification and verification performance, evaluate performance with respect to pose and a persons age, and compare as a function of training data size (#photos and #people). We report results of state of the art and baseline algorithms. The MegaFace dataset, baseline code, and evaluation scripts, are all publicly released for further experimentations1.
Game Development for Computer Science Education

Educators have long used digital games as platforms for teaching. Games tend to have several qualities that aren't typically found in homework: they often situate problems within a compelling alternate reality that unfolds through intriguing narrative, they often draw more upon a player's intrinsic motivations than extrinsic ones, they can facilitate deliberate low intensity practice, and they often emphasize a spirit of play instead of work.
 At ITiCSE 2016, this working group convened to survey the landscape of existing digital games that have been used to teach and learn computer science concepts. Our group discovered that these games lacked explicitly defined learning goals and even less evaluation of whether or not the games achieved these goals. As part of this process, we identified and played over 120 games that have been released or described in literature as means for learning computer science concepts. In our report, we classified how these games support the learning objectives outlined in the ACM/IEEE Computer Science Curricula 2013.
 While we found more games than we expected, few games explicitly stated their learning goals and even fewer were evaluated for their capacity to meet these goals. Most of the games we surveyed fell into two categories: short-lived proof-of-concept projects built by academics or closed-source games built by professional developers. Gathering adequate learning data is challenging in either situation. Our original intent for the second year of our working group was to prepare a comprehensive framework for collecting and analyzing learning data from computer science learning games. Upon further discussion, however, we decided that a better next step is to validate the design and development guidelines that we put forth in our final report for ITiCSE 2016.
 We extend this working group to a second year---with a mission to collaboratively develop a game with clearly defined learning objectives and define a methodology for evaluating its capacity to meet its goals.
Robot control design using virtual path from android smartphone

Progress robot development very varied almost all fields seize the attention of the world's scientists and researchers. For some cases, robots are generally used to help humans in relieving job. The robot can replace human mobilization for move so that the efficiency of wasted time will be less. Many things need to be note what kind of robot control reliable to replace human work in mobilization. Virtual world as in android smartphone greatly benefit humans to creating control tools easy to use anywhere and could represent a desire humans. By making control robot using virtual paths on the Android smartphone which have an accuracy of more than 70% to a scale of 1:50 with the actual track in an environment that is not wide enough, the advantage can be used to control the robot move easily and efficiently compared to the usual manual control robot that will take more time to control The robot moves its place as desired.
Character-Based Text Classification using Top Down Semantic Model for Sentence Representation

Despite the success of deep learning on many fronts especially image and speech, its application in text classification often is still not as good as a simple linear SVM on n-gram TF-IDF representation especially for smaller datasets. Deep learning tends to emphasize on sentence level semantics when learning a representation with models like recurrent neural network or recursive neural network, however from the success of TF-IDF representation, it seems a bag-of-words type of representation has its strength. Taking advantage of both representions, we present a model known as TDSM (Top Down Semantic Model) for extracting a sentence representation that considers both the word-level semantics by linearly combining the words with attention weights and the sentence-level semantics with BiLSTM and use it on text classification. We apply the model on characters and our results show that our model is better than all the other characterbased and word-based convolutional neural network models by (Zhang et al., 2015) across seven different datasets with only 1% of their parameters. We also demonstrate that this model beats traditional linear models on TF-IDF vectors on small and polished datasets like news article in which typically deep learning models surrender.
Integrating UML, MARTE and sysml to improve requirements specification and traceability in the embedded domain

This work presents a model-driven requirement engineering approach for the embedded software domain. This approach is based on UML, MARTE and SysML standard notations, which are integrated in order to improve requirements specification and traceability. MARTE stereotypes are mainly used to specify non-functional requirements, exceptionally imperative in this domain, while SysML notations integrated to UML models support requirements management. The management is based on traceability matrixes, which are automatically generated from SysML requirements diagrams. Furthermore, we illustrate the effectiveness of our approach by means of a case study.
DG allocation for benefit maximization in distribution networks

This paper proposes a method to evaluate the worth of installing renewable distributed generation (DG) in distribution networks. Moreover, the work optimally allocates these DG units in the distribution network to maximize the worth of the connection to the local distribution company (LDC), as well as the customers connected to the system. The proposed methodology helps the LDC to better assess the benefits of the renewable DG units' proposed connections and to identify the optimal buses on which to connect these DG units. The benefits considered in this paper are deferral of upgrade investments, reduction of the cost of energy losses, and reliability improvement, which is represented by the interruption cost reduction. The proposed methodology takes into consideration the uncertainty and variability associated with the output power of renewable DG as well as the load variability. The planning problem of determining the optimal location and sizes of DG units is defined as multi-objective mixed integer programming.
Gain and bandwidth improvement of microstrip antenna using RIS and FPC resonator

Improvement in gain as well as bandwidth of microstrip antenna using reactive impedance surface and Fabry Perot cavity resonator is proposed. Suspended microstrip antenna (MSA) is designed on reactive impedance surface (RIS) backed FR4 substrate. RIS improves bandwidth, gain and efficiency of antenna due to reduced coupling between MSA and ground plane. This MSA backed RIS is placed in a FPC resonator for further improving the gain of MSA. 1×1 to 4×4 square patch arrays are designed on a dielectric layer which is energized by the radiations from RIS backed MSA. The PRS layer is placed at about λ0/2 from ground. The S11 is < −9.5 dB over 5.725–6.4 GHz. Maximum gain of 15.8 dBi with gain variation < 2 dB is obtained using 4×4 array. Beside this, cross polarization and Side lobe level are < −2ο dB. Antenna offers front to back lobe ratio (FBR) > 20 dB and > 70 % antenna efficiency. The measured results of prototype structure are in agreement with the simulation results.
Enhancing linear system theory curriculum with an inverted pendulum robot

The demands on both delivery methodology and content of control curriculum continue to evolve as more applications in our world incorporate control theory and the need for continuing education increases. Not only has the content evolved, but the application of social-behavioral science research has resulted in cooperative and active learning practices by faculty. In response to these shifts in education, an open-source inverted pendulum robot was used in a linear systems theory (LST) class taught as part of a professional master's program (PMP). The robot had to have a low cost, and enough capability to enable the students to explore and test the ideas presented in lecture and to engage in collaborative learning experiences. This paper discusses the robot, describes the key control theory experiments, and reviews the lessons learned from this experience.
Inertial properties of the human trunk of males determined from magnetic resonance imaging

The purpose of this study was to evaluate the segmental parameters of the human trunk of malesin vivo using magnetic resonance imaging (MRI). In addition, the efficacy of volumetric estimation and existing prediction formulas to produce segmental properties similar to those produced by MRI was evaluated. As opposed to finding one representative normal value for these parameters, a range of normal values was defined. For instance, the average trunk mass was 42.2%±3.5% (x±SD) of body mass, but values ranged from 35.8% to 48.0%. To account for segment parameters more accurately, specific anthropometric measures need to be considered in addition to overall measures of body height and mass. These specific measures included segment length, circumference, width, and depth. Studies reporting general percentages based on height and/or mass were found to be inadequate predictors of segmental parameters of the trunk compared with MRI estimates. Volumebased estimates, which assume a uniform density distribution within a segment, were found to correspond closely to MRI values except for the thorax. However, the use of density values reflective of the livingin vivo state would likely alleviate this disparity, thus indicating that the volumetric technique may be effective for deriving segmental parameters for large segments of the trunk. Future research should adopt noninvasive techniques such as MRI and/or volumetric estimation to enhance the predictability of segmental parameters of the body for specific population groups characterized by gender, developmental age, body type, and fitness level. Further efforts should be made to establish standardized boundary definitions for trunk segments to avoid unnecessary confusion, from which substantial errors may be introduced into biomechanical linked-segment analyses of human movement.
Are Your Neighbors Swingers or Kinksters? Feeld App Forensic Analysis

Dating applications (apps) are becoming more niche and intimate, allowing users interested in alternative sexual lifestyles to discover each other in relation to their geo-location as well as mutual sexual interests. Such apps can provide a wealth of information in a forensic investigation, although research in this topic appears limited. This paper examines the extent and type of artifacts created on a device when using Feeld, an app whose user base is unconventional either by their sexual identity or by an inclination to experience alternative sexual activities, and their impact on the privacy of users sharing the most personal details of their sexual lives. Such users may be at a higher risk of victimization due to their sexual identity or orientation. Specifically, we demonstrate what types of data can be forensically recovered (e.g. messages to and from user account, and photos belonging to other dating app users), and how distance information stored by the app can be used to conduct a trilateration attack and successfully expose another user's actual location.
Graph-based distributed cooperative navigation for a general multi-robot measurement model

Cooperative navigation (CN) enables a group of cooperative robots to reduce their individual navigation errors. For a general multi-robot (MR) measurement model that involves both inertial navigation data and other onboard sensor readings, taken at different time instances, the various sources of information become correlated. Thus, this correlation should be solved for in the process of information fusion to obtain consistent state estimation. The common approach for obtaining the correlation terms is to maintain an augmented covariance matrix. This method would work for relative pose measurements, but is impractical for a general MR measurement model, because the identities of the robots involved in generating the measurements, as well as the measurement time instances, are unknown a priori. In the current work, a new consistent information fusion method for a general MR measurement model is developed. The proposed approach relies on graph theory. It enables explicit on-demand calculation of the required correlation terms. The graph is locally maintained by every robot in the group, representing all the MR measurement updates. The developed method calculates the correlation terms in the most general scenarios of MR measurements while properly handling the involved process and measurement noise. A theoretical example and a statistical study are provided, demonstrating the performance of the method for vision-aided navigation based on a three-view measurement model. The method is compared, in a simulated environment, to a fixed-lag centralized smoothing approach. The method is also validated in an experiment that involved real imagery and navigation data. Computational complexity estimates show that the newly-developed method is computationally efficient.
Trefoil Torus Knot Monopole Antenna

A wideband and simple torus knot monopole antenna is presented in this letter. The antenna is fabricated using additive manufacturing technology, commonly known as 3-D printing. The antenna is mechanically simple to fabricate and has stable radiation pattern as well as input reflection coefficient below -10 dB over a frequency range of 1-2 GHz. A comparison of measured and simulated performance of the antenna is also presented.
Personalized news recommendation based on click behavior

Online news reading has become very popular as the web provides access to news articles from millions of sources around the world. A key challenge of news websites is to help users find the articles that are interesting to read. In this paper, we present our research on developing personalized news recommendation system in Google News. For users who are logged in and have explicitly enabled web history, the recommendation system builds profiles of users' news interests based on their past click behavior. To understand how users' news interests change over time, we first conducted a large-scale analysis of anonymized Google News users click logs. Based on the log analysis, we developed a Bayesian framework for predicting users' current news interests from the activities of that particular user and the news trends demonstrated in the activity of all users. We combine the content-based recommendation mechanism which uses learned user profiles with an existing collaborative filtering mechanism to generate personalized news recommendations. The hybrid recommender system was deployed in Google News. Experiments on the live traffic of Google News website demonstrated that the hybrid method improves the quality of news recommendation and increases traffic to the site.
Image classification using the web graph

Image classification is a well-studied and hard problem in computer vision. We extend a proven solution for classifying web spam to handle images. We exploit the link structure of the web graph: a web page related to a given category is normally linked to other pages describing related objects. Our approach combines information from the webgraph structure with semi-supervised learning from all the unlabeled images to create a superior image-classification model for multimedia data. We show that fusing image, text and web-graph features gives a 12% improvement (in the area under the ROC curve) over content features alone in an adult image-classification experiment.
ProfessorJ: a gradual introduction to Java through language levels

In the second-semester programming course at the University of Utah, we have observed that our students suffer unnecessarily from a mismatch between the course content and the programming environment. The course is typical, in that it exposes students to Java a little at a time. The programming environments are also typical, in that they report compilation and run-time errors in the jargon of professional programmers who use the full Java language. As a result, students rely heavily on teaching assistants to interpret error messages, and valuable classroom time is wasted on syntactic diversions.ProfessorJ is our new programming environment that remedies this problem. Like other pedagogical environments, such as BlueJ and DrJava, ProfessorJ presents the student with a simplified interface to the Java compiler and virtual machine. Unlike existing environments, ProfessorJ tailors the Java language and error messages to the students' needs. Since their needs evolve through the course, ProfessorJ offers several language levels, from Beginner Java to
The Statistical Recurrent Unit

Sophisticated gated recurrent neural network architectures like LSTMs and GRUs have been shown to be highly effective in a myriad of applications. We develop an un-gated unit, the statistical recurrent unit (SRU), that is able to learn long term dependencies in data by only keeping moving averages of statistics. The SRU’s architecture is simple, un-gated, and contains a comparable number of parameters to LSTMs; yet, SRUs perform favorably to more sophisticated LSTM and GRU alternatives, often outperforming one or both in various tasks. We show the efficacy of SRUs as compared to LSTMs and GRUs in an unbiased manner by optimizing respective architectures’ hyperparameters in a Bayesian optimization scheme for both synthetic and realworld tasks.