nils-herrmann/clean_scirepeval huggingface dataset:
For a social network analysis of computer networks: a sociological perspective on collaborative work and virtual community

When computer networks link people as well as machines, they become social networks. Social network analysis provides a useful approach to moving beyond the concept of “group” in studying virtual communities and computer supported cooperative work and telework. Such computer supported social networks (CSSNS) sustain strong, intermediate and weak ties that provide information and social support in both specialized and broadly-based relationships. They foster informal workplace communities that are usually partial and narrowly-focused, although some do become encompassing and broadly-based. CSSNS connect workers within and between organizations who often are physically dispersed. The nature of the medium both constrains and facilitates management control. Although many relationships function offline as well as online, CSSNS have developed their own norms and structures. They have strong societal implications, fostering situations that combine global connectivity, the fragmentation of solidarities and the de-emphasis of local organization, and the importance of home bases.
High Confidence Policy Improvement

We present a batch reinforcement learning (RL) algorithm that provides probabilistic guarantees about the quality of each policy that it proposes, and which has no hyper-parameters that require expert tuning. The user may select any performance lower-bound, ρ−, and confidence level, δ, and our algorithm will ensure that the probability that it returns a policy with performance below ρ− is at most δ. We then propose an incremental algorithm that executes our policy improvement algorithm repeatedly to generate multiple policy improvements. We show the viability of our approach with a simple gridworld and the standard mountain car problem, as well as with a digital marketing application that uses real world data.
Additive manufacturing of polymer-derived ceramics

The extremely high melting point of many ceramics adds challenges to additive manufacturing as compared with metals and polymers. Because ceramics cannot be cast or machined easily, three-dimensional (3D) printing enables a big leap in geometrical flexibility. We report preceramic monomers that are cured with ultraviolet light in a stereolithography 3D printer or through a patterned mask, forming 3D polymer structures that can have complex shape and cellular architecture. These polymer structures can be pyrolyzed to a ceramic with uniform shrinkage and virtually no porosity. Silicon oxycarbide microlattice and honeycomb cellular materials fabricated with this approach exhibit higher strength than ceramic foams of similar density. Additive manufacturing of such materials is of interest for propulsion components, thermal protection systems, porous burners, microelectromechanical systems, and electronic device packaging.
A study on human gait analysis

Human gait is one of the most important biometric which has so far been neglected for use in medical diagnostics. In this paper, we make a feasibility study on human gait acquired from a wearable sensor based biometric suit called as Intelligent Gait Oscillation Detector (IGOD). This suit measures simultaneous gait oscillation from eight major joints (two knees, two hips, two elbows and two shoulders) of a human body. Techniques for analyzing and understanding the human gait patterns were developed. Variance in the gait oscillation was studied with respect to gait speed varying from 3km/hr to 5km/hr. Gender variance (male/female) gait oscillation has also been studied. A comprehensive analysis on human gait affected by knee joint movement and hip joint oscillation has been addressed with the arm swing effects. This analysis will provide us with an insight on human bipedal locomotion and its stability. We plan to create a repository of human gait oscillations which could extensively be analyzed for person identification and detecting walking problems in patients, which is detection of disease in the medical field.
Gamification in Fitness Apps: How Do Leaderboards Influence Exercise?

While leaderboards are a key gamification element incorporated in fitness apps to spur competition among users, various issues exist in their use. Yet, the impact of leaderboards and the social comparison they entail in these apps is under-studied and not well understood. Motivated thus, we build a theoretical model to explain their impact on the attitude and physical activity behavior of users by drawing on social comparison theory. We also propose that environmental (perceived competitive climate) and individual (self-efficacy) factors moderate the relationship between social comparison via leaderboards and users attitude. The model was tested by collecting survey and objective website data from 80 users of Nike+ Running. Our results show that users’ attitude and facilitating conditions increase their physical activity behavior. Additionally, perceived competitive climate and self-efficacy moderate the effect of social comparison on users’ attitude in opposing directions. The expected contributions and remaining research plan are described.
Far-Field Prediction Using Only Magnetic Near-Field Scanning for EMI Test

Far-field prediction for electromagnetic interference (EMI) testing is achieved using only magnetic near-field on a Huygens's surface. The electrical field on the Huygens's surface is calculated from the magnetic near-field using the finite element method (FEM). Two examples are used to verify the proposed method. The first example uses the field radiated by an infinitesimal electric dipole. The calculated results are compared with the analytical solution. In the second example, the calculated results are compared with full-wave simulation results for the radiation of a print circuit board (PCB). The validity of this method when the near-field is high-impedance field is verified as well. Sensitivity of the far field to noise in both magnitude and phase in the near-field data is also investigated. The results indicate that the proposed method is very robust to the random variation of both. The effect of using only four sides of the Huygens's box is investigated as well, revealing that, in some instances, the incomplete Huygens's box can be used to predict the far field well. The proposed method is validated using near-field measurement data taken from a sleeve dipole antenna. The error for the maximum far-field value is in only 1.3 dB.
Strabismus Recognition Using Eye-Tracking Data and Convolutional Neural Networks

Strabismus is one of the most common vision diseases that would cause amblyopia and even permanent vision loss. Timely diagnosis is crucial for well treating strabismus. In contrast to manual diagnosis, automatic recognition can significantly reduce labor cost and increase diagnosis efficiency. In this paper, we propose to recognize strabismus using eye-tracking data and convolutional neural networks. In particular, an eye tracker is first exploited to record a subject's eye movements. A gaze deviation (GaDe) image is then proposed to characterize the subject's eye-tracking data according to the accuracies of gaze points. The GaDe image is fed to a convolutional neural network (CNN) that has been trained on a large image database called ImageNet. The outputs of the full connection layers of the CNN are used as the GaDe image's features for strabismus recognition. A dataset containing eye-tracking data of both strabismic subjects and normal subjects is established for experiments. Experimental results demonstrate that the natural image features can be well transferred to represent eye-tracking data, and strabismus can be effectively recognized by our proposed method.
MELOGRAPH: Multi-Engine WorkfLOw Graph Processing

This paper introduces MELOGRAPH, a new system that exposes in the front-end a domain specific language(DSL) for graph processing tasks and in the back-end identifies, ranks and generates source code for the top-N ranked engines. This approach lets the specialized MELOGRAPH be part of a more general multi-engine workflow optimizer. The candidate execution engines are chosen from the contemporaneous Big Data ecosystem: graph databases (e.g. Neo4j, TitanDB, OrientDB, Sparksee/DEX) and robust graph processing frameworks with Java API or packaged libraries of algorithms (e.g. Giraph, Okapi, Flink Gelly, Hama, Gremlin). As MELOGRAPH is work in progress, our current paper stresses upon the state of the art in this field, provides a general architecture and some early implementation insights.
Individual differences in social comparison: development of a scale of social comparison orientation.

Development and validation of a measure of individual differences in social comparison orientation (the Iowa-Netherlands Comparison Orientation Measure [INCOM]) are described. Assuming that the tendency toward social comparison is universal, the scale was constructed so as to be appropriate to and comparable in 2 cultures: American and Dutch. It was then administered to several thousand people in each country. Analyses of these data are presented indicating that the scale has good psychometric properties. In addition, a laboratory study and several field studies are described that demonstrated the INCOM's ability to predict comparison behavior effectively. Possible uses of the scale in basic and applied settings are discussed.
Unsupervised Word Alignment by Agreement Under ITG Constraint

We propose a novel unsupervised word alignment method that uses a constraint based on Inversion Transduction Grammar (ITG) parse trees to jointly unify two directional models. Previous agreement methods are not helpful for locating alignments with long distances because they do not use any syntactic structures. In contrast, the proposed method symmetrizes alignments in consideration of their structural coherence by using the ITG constraint softly in the posterior regularization framework (Ganchev et al., 2010). The ITG constraint is also compatible with word alignments that are not covered by ITG parse trees. Hence, the proposed method is robust to ITG parse errors compared to other alignment methods that directly use an ITG model. Compared to the HMM (Vogel et al., 1996), IBM Model 4 (Brown et al., 1993), and the baseline agreement method (Ganchev et al., 2010), the experimental results show that the proposed method significantly improves alignment performance regarding the Japanese-English KFTT and BTEC corpus, and in translation evaluation, the proposed method shows comparable or statistical significantly better performance on the JapaneseEnglish KFTT and IWSLT 2007 corpus.
Effective Heart Sound Segmentation and Murmur Classification Using Empirical Wavelet Transform and Instantaneous Phase for Electronic Stethoscope

Accurate measurement of heart sound and murmur parameters is of great importance in the automated analysis of phonocardiogram (PCG) signals. In this paper, we propose a novel unified PCG signal delineation and murmur classification method without the use of reference signal for automatic detection and classification of heart sounds and murmurs. The major components of the proposed method are the empirical wavelet transform-based PCG signal decomposition for discriminating heart sounds from heart murmurs and suppressing background noises, the Shannon entropy envelope extraction, the instantaneous phase-based boundary determination, heart sound and murmur parameter extraction, the systole/diastole discrimination and the decision rules-based murmur classification. The accuracy and robustness of the proposed method is evaluated using a wide variety of normal and abnormal PCG signals taken from the standard PCG databases, including PASCAL heart sounds challenge database, PhysioNet/CinC challenge heart sound database, and real-time PCG signals. Evaluation results show that the proposed method achieves an average sensitivity (Se) of 94.38%, positive predictivity (Pp) of 97.25%, and overall accuracy (OA) of 91.92% for heart sound segmentation and Se of 97.58%, Pp of 96.46%, and OA of 94.21% in detecting the presence of heart murmurs for SNR of 10 dB. The method yields an average classification accuracy of 95.5% for the PCG signals with SNR of 20 dB. Results show that the proposed method outperforms other existing heart sound segmentation and murmur classification methods.
Automatic feature extraction and selection for condition monitoring and related datasets

In this paper a combination of methods for feature extraction and selection is proposed suitable for extracting highly relevant features for machine condition monitoring and related applications from time domain, frequency domain, time-frequency domain and the statistical distribution of the measurement values. The approach is fully automated and suitable for multiple condition monitoring tasks such as vibration and process sensor based analysis. This versatility is demonstrated by evaluating two condition monitoring datasets from our own experiments plus multiple freely available time series classification tasks and comparing the achieved results with the results of algorithms previously suggested or even specifically designed for these datasets.
A high-speed end-to-end approach for retinal arteriovenous segmentation

Retinal artery/vein segmentation plays a significant role in retinal relevant diseases diagnosis. This paper presents a novel, end-to-end and multi-task convolutional neural network structure for retinal arteriovenous segmentation task. The proposed method can simultaneously output the same size as the original image of the artery map and the vein map, respectively. Then we evaluate the neural network on the AVDRIVE dataset. This method achieves sensitivity values of 0.86, specificity values of 0.82, precision values of 0.87, F1score values of 0.86 and accuracy values of 0.94. In this case the time it cost on segment one image only needed 0.13 seconds, which are 60 times faster than other studies we cite.
Generating Production Rules from Decision Trees

Many inductive knowledge acquisition algorithms generate classifiers in the form of decision trees. This paper describes a technique for transforming such trees to small sets of production rules, a common formalism for expressing knowledge in expert systems. The method makes use of the training set of cases from which the decision tree was generated, first to generalize and assess the reliability of individual rules extracted from the tree, and subsequently to refine the collection of rules as a whole. The final set of production rules is usually both simpler than the decision tree from which it was obtained, and more accurate when classifying unseen cases. Transformation to production rules also provides a way of combining different decision trees for the same classification domain.
Robust Cross-lingual Hypernymy Detection using Dependency Context

Cross-lingual Hypernymy Detection involves determining if a word in one language (“fruit”) is a hypernym of a word in another language (“pomme” i.e. apple in French). The ability to detect hypernymy cross-lingually can aid in solving cross-lingual versions of tasks such as textual entailment and event coreference. We propose BISPARSE-DEP, a family of unsupervised approaches for cross-lingual hypernymy detection, which learns sparse, bilingual word embeddings based on dependency contexts. We show that BISPARSE-DEP can significantly improve performance on this task, compared to approaches based only on lexical context. Our approach is also robust, showing promise for low-resource settings: our dependency-based embeddings can be learned using a parser trained on related languages, with negligible loss in performance. We also crowd-source a challenging dataset for this task on four languages – Russian, French, Arabic, and Chinese. Our embeddings and datasets are publicly
Open-set Plant Identification Using an Ensemble of Deep Convolutional Neural Networks

Open-set recognition, a challenging problem in computer vision, is concerned with identification or verification tasks where queries may belong to unknown classes. This work describes a fine-grained plant identification system consisting of an ensemble of deep convolutional neural networks, within an open-set identification framework. Two wellknown deep learning architectures of VGGNet and GoogLeNet, pretrained on the object recognition dataset of ILSVRC 2012, are fine-tuned using the plant dataset of LifeCLEF 2015. Moreover, GoogLeNet is finetuned using plant and non-plant images, to serve for rejecting samples from non-plant classes. Our systems have been evaluated on the test dataset of PlantCLEF 2016 by the campaign organizers and the best proposed model has achieved an official score of 0.738 in terms of the mean average precision, while the best official score was announced to be 0.742.
On solving multiobjective bin packing problems using evolutionary particle swarm optimization

The bin packing problem is widely found in applications such as loading of tractor trailer trucks, cargo airplanes and ships, where a balanced load provides better fuel efficiency and safer ride. In these applications, there are often conflicting criteria to be satisfied, i.e., to minimize the bins used and to balance the load of each bin, subject to a number of practical constraints. Unlike existing studies that only consider the issue of minimum bins, a multiobjective two-dimensional mathematical model for bin packing problems with multiple constraints (MOBPP-2D) is formulated in this paper. To solve MOBPP-2D problems, a multiobjective evolutionary particle swarm optimization algorithm (MOEPSO) is proposed. Without the need of combining both objectives into a composite scalar weighting function, MOEPSO incorporates the concept of Pareto’s optimality to evolve a family of solutions along the trade-off surface. Extensive numerical investigations are performed on various test instances, and their performances are compared both quantitatively and statistically with other optimization methods to illustrate the effectiveness and efficiency of MOEPSO in solving multiobjective bin packing problems.
A COMPARATIVE STUDY OF CLASSIFICATION ALGORITHMS FOR CREDIT CARD APPROVAL USING WEKA

The growing requirement of data mining applications based on classification in financial and other areas has shown the need that data mining algorithms need to be applied for better decision making. The increasing volume of data is also becoming a challenge for data mining community. In this paper, four classification algorithms have been comparatively tested to find the optimum algorithm for classification. The Credit Card Approval dataset has been used for experimental purposes that contain 690 instances with 15 attributes and 1 class attribute to test and justify the differences among classification algorithms. The four classification algorithms are – Decision Tree (DT), Naïve Bayes‟ (NB), Artificial Neural Network (ANN) and Support Vector Machine (SVM). The WEKA (Waikato Environment for Knowledge Analysis) tool which is open source software is used for simulation. Subsequently, the classification technique that has the potential to significantly improve the common or conventional methods will be suggested for use in financial applications.
Towards Blockchain-enabled School Information Hub

Several initiatives have been proposed to collect, report, and analyze data about school systems for supporting decision-making. These initiatives rely mostly on self-reported and summarized data collected irregularly and rarely. They also lack a single independent and systematic process to validate the collected data during its entire lifecycle. Furthermore, schools in developing countries still do not maintain complete and up-to-date school records. Due to these and other factors addressing the education challenges in those countries remains a high priority for local and international governments, donor and non-governmental agencies across the world. In this paper, we discuss our initial design, implementation, and evaluation of a blockchain-enabled School Information Hub (SIH) using Kenya's school system as a case study.
BASIS: A big data architecture for smart cities

Nowadays, cities are the common choice for living, representing a complex system where governments need to perform adequately, despite current restrictions, in order to satisfy the needs of the citizens and overcome economic, social and environmental sustainability challenges. The Smart City term emerges to conceptualize the need to understand citizens, namely their services demand and their relevance in a participatory government. Smart Cities are known for their human dynamics, which makes recurrent use of permanently connected devices, frequently known as Internet of Things (IoT). Consequently, since these new cities generate a vast volume of data with significant variety and velocity, they have the potential to be one of the richest and challenging systems to generate Big Data and to benefit from its adequate storage, processing, analysis and public availability. This paper presents a Big Data architecture for Smart Cities, entitled BASIS, whose specification pays particular attention to the creation of multiple abstraction layers, from the most conceptual to the most technological, fulfilling the lack of technological detail often observed in the literature. BASIS also pays particular attention to the public availability of data. Tested in a demonstration case, the obtained results reveal adequate capability to store, process, analyse and make available Big Data in the context of Smart Cities.
Induction of mitochondrial-mediated apoptosis by Morinda citrifolia (Noni) in human cervical cancer cells.

Cervical cancer is the second most common cause of cancer in women and has a high mortality rate. Cisplatin, an antitumor agent, is generally used for its treatment. However, the administration of cisplatin is associated with side effects and intrinsic resistance. Morinda citrifolia (Noni), a natural plant product, has been shown to have anti-cancer properties. In this study, we used Noni, cisplatin, and the two in combination to study their cytotoxic and apoptosis-inducing effects in cervical cancer HeLa and SiHa cell lines. We demonstrate here, that Noni/Cisplatin by themselves and their combination were able to induce apoptosis in both these cell lines. Cisplatin showed slightly higher cell killing as compared to Noni and their combination showed additive effects. The observed apoptosis appeared to be mediated particularly through the up-regulation of p53 and pro-apoptotic Bax proteins, as well as down- regulation of the anti-apoptotic Bcl-2, Bcl-XL proteins and survivin. Augmentation in the activity of caspase-9 and -3 was also observed, suggesting the involvement of the intrinsic mitochondrial pathway of apoptosis for both Noni and Cisplatin in HeLa and SiHa cell lines.
HeidelPlace: An Extensible Framework for Geoparsing

Geographic information extraction from textual data sources, called geoparsing, is a key task in text processing and central to subsequent spatial analysis approaches. Several geoparsers are available that support this task, each with its own (often limited or specialized) gazetteer and its own approaches to toponym detection and resolution. In this demonstration paper, we present HeidelPlace, an extensible framework in support of geoparsing. Key features of HeidelPlace include a generic gazetteer model that supports the integration of place information from different knowledge bases, and a pipeline approach that enables an effective combination of diverse modules tailored to specific geoparsing tasks. This makes HeidelPlace a valuable tool for testing and evaluating different gazetteer sources and geoparsing methods. In the demonstration, we show how to set up a geoparsing workflow with HeidelPlace and how it can be used to compare and consolidate the output of different geoparsing approaches.