nils-herrmann/clean_scirepeval huggingface dataset:
A typology of crowdfunding sponsors: Birds of a feather flock together?

This study develops a crowdfunding sponsor typology based on sponsors’ motivations for participating in a project. Using a two by two crowdfunding motivation framework, we analyzed six relevant funding motivations—interest, playfulness, philanthropy, reward, relationship, and recognition—and identified four types of crowdfunding sponsors: angelic backer, reward hunter, avid fan, and tasteful hermit. They are profiled in terms of the antecedents and consequences of funding motivations. Angelic backers are similar in some ways to traditional charitable donors while reward hunters are analogous to market investors; thus they differ in their approach to crowdfunding. Avid fans comprise the most passionate sponsor group, and they are similar to members of a brand community. Tasteful hermits support their projects as actively as avid fans, but they have lower extrinsic and others-oriented motivations. The results show that these sponsor types reflect the nature of crowdfunding as a new form of co-creation in the E-commerce context.
AGV global localization using indistinguishable artificial landmarks

In this paper we consider the global localization problem for an industrial AGV moving in a known environment. The problem consists of determining the pose of the vehicle without any prior information about its location. The vehicle is supposed to be equipped with a laser scanner that allows to measure the range and bearing of the vehicle with respect to a set of anonymous landmarks. A map with the positions of all landmarks in the environment is available to the localization system. We propose a novel algorithm for AGV self-localization based on landmarks identification that can take into account also false detections, very common in industrial environments. The pose is computed with a single scan (2D), without any sensor fusion. The performance of the proposed strategy is shown both by simulations and experiments on real industrial plants.
3D printed soft actuators for a legged robot capable of navigating unstructured terrain

Soft robots have recently demonstrated impressive abilities to adapt to objects and their environment with limited sensing and actuation. However, mobile soft robots are typically fabricated using laborious molding processes that result in limited actuated degrees of freedom and hence limited locomotion capabilities. In this paper, we present a 3D printed robot with bellowed soft legs capable of rotation about two axes. This allows our robot to navigate rough terrain that previously posed a significant challenge to soft robots. We present models and FEM simulations for the soft leg modules and predict the robot locomotion capabilities. We use finite element analysis to simulate the actuation characteristics of these modules. We then compared the analytical and computational results to experimental results with a tethered prototype. The experimental soft robot is capable of lifting its legs 5.3 cm off the ground and is able to walk at speeds up to 20 mm/s (0.13 bl/s). This work represents a practical approach to the design and fabrication of functional mobile soft robots.
Distance Metric Learning : A Comprehensive Survey

Many machine learning algorithms, such as K Nearest Neighbor (KNN), heavily rely on the distance metric for the input data patterns. Distance Metric learning is to learn a distance metric for the input space of data from a given collection of pair of similar/dissimilar points that preserves the distance relation among the training data. In recent years, many studies have demonstrated, both empirically and theoretically, that a learned metric can significantly improve the performance in classification, clustering and retrieval tasks. This paper surveys the field of distance metric learning from a principle perspective, and includes a broad selection of recent work. In particular, distance metric learning is reviewed under different learning conditions: supervised learning versus unsupervised learning, learning in a global sense versus in a local sense; and the distance matrix based on linear kernel versus nonlinear kernel. In addition, this paper discusses a number of techniques that is central to distance metric learning, including convex programming, positive semi-definite programming, kernel learning, dimension reduction, K Nearest Neighbor, large margin classification, and graph-based approaches.
Clustering data streams using grid-based synopsis

Continually advancing technology has made it feasible to capture data online for onward transmission as a steady flow of newly generated data points, termed as data stream. Continuity and unboundedness of data streams make storage of data and multiple scans of data an impractical proposition for the purpose of knowledge discovery. Need to learn structures from data in streaming environment has been a driving force for making clustering a popular technique for knowledge discovery from data streams. Continuous nature of streaming data makes it infeasible to look for point membership among the clusters discovered so far, necessitating employment of a synopsis structure to consolidate incoming data points. This synopsis is exploited for building clustering scheme to meet subsequent user demands. The proposed Exclusive and Complete Clustering (ExCC) algorithm captures non-overlapping clusters in data streams with mixed attributes, such that each point either belongs to some cluster or is an outlier/noise. The algorithm is robust, adaptive to changes in data distribution and detects succinct outliers on-the-fly. It deploys a fixed granularity grid structure as synopsis and performs clustering by coalescing dense regions in grid. Speed-based pruning is applied to synopsis prior to clustering to ensure currency of discovered clusters. Extensive experimentation demonstrates that the algorithm is robust, identifies succinct outliers on-the-fly and is adaptive to change in the data distribution. ExCC algorithm is further evaluated for performance and compared with other contemporary algorithms.
Online Discovery of Gathering Patterns over Trajectories

The increasing pervasiveness of location-acquisition technologies has enabled collection of huge amount of trajectories for almost any kind of moving objects. Discovering useful patterns from their movement behaviors can convey valuable knowledge to a variety of critical applications. In this light, we propose a novel concept, called gathering, which is a trajectory pattern modeling various group incidents such as celebrations, parades, protests, traffic jams and so on. A key observation is that these incidents typically involve large congregations of individuals, which form durable and stable areas with high density. In this work, we first develop a set of novel techniques to tackle the challenge of efficient discovery of gathering patterns on archived trajectory dataset. Afterwards, since trajectory databases are inherently dynamic in many real-world scenarios such as traffic monitoring, fleet management and battlefield surveillance, we further propose an online discovery solution by applying a series of optimization schemes, which can keep track of gathering patterns while new trajectory data arrive. Finally, the effectiveness of the proposed concepts and the efficiency of the approaches are validated by extensive experiments based on a real taxicab trajectory dataset.
Spectral clustering for multi-type relational data

Clustering on multi-type relational data has attracted more and more attention in recent years due to its high impact on various important applications, such as Web mining, e-commerce and bioinformatics. However, the research on general multi-type relational data clustering is still limited and preliminary. The contribution of the paper is three-fold. First, we propose a general model, the collective factorization on related matrices, for multi-type relational data clustering. The model is applicable to relational data with various structures. Second, under this model, we derive a novel algorithm, the spectral relational clustering, to cluster multi-type interrelated data objects simultaneously. The algorithm iteratively embeds each type of data objects into low dimensional spaces and benefits from the interactions among the hidden structures of different types of data objects. Extensive experiments demonstrate the promise and effectiveness of the proposed algorithm. Third, we show that the existing spectral clustering algorithms can be considered as the special cases of the proposed model and algorithm. This demonstrates the good theoretic generality of the proposed model and algorithm.
Capturing AU-Aware Facial Features and Their Latent Relations for Emotion Recognition in the Wild

The Emotion Recognition in the Wild (EmotiW) Challenge has been held for three years. Previous winner teams primarily focus on designing specific deep neural networks or fusing diverse hand-crafted and deep convolutional features. They all neglect to explore the significance of the latent relations among changing features resulted from facial muscle motions. In this paper, we study this recognition challenge from the perspective of analyzing the relations among expression-specific facial features in an explicit manner. Our method has three key components. First, we propose a pair-wise learning strategy to automatically seek a set of facial image patches which are important for discriminating two particular emotion categories. We found these learnt local patches are in part consistent with the locations of expression-specific Action Units (AUs), thus the features extracted from such kind of facial patches are named AU-aware facial features. Second, in each pair-wise task, we use an undirected graph structure, which takes learnt facial patches as individual vertices, to encode feature relations between any two learnt facial patches. Finally, a robust emotion representation is constructed by concatenating all task-specific graph-structured facial feature relations sequentially. Extensive experiments on the EmotiW 2015 Challenge testify the efficacy of the proposed approach. Without using additional data, our final submissions achieved competitive results on both sub-challenges including the image based static facial expression recognition (we got 55.38% recognition accuracy outperforming the baseline 39.13% with a margin of 16.25%) and the audio-video based emotion recognition (we got 53.80% recognition accuracy outperforming the baseline 39.33% and the 2014 winner team's final result 50.37% with the margins of 14.47% and 3.43%, respectively).
Generating Realistic Impressions for File-System Benchmarking

The performance of file systems and related software depends on characteristics of the underlying file-system image (i.e., file-system metadata and file contents). Unfortunately, rather than benchmarking with realistic file-system images, most system designers and evaluators rely on ad hoc assumptions and (often inaccurate) rules of thumb. Furthermore, the lack of standardization and reproducibility makes file-system benchmarking ineffective. To remedy these problems, we develop Impressions, a framework to generate statistically accurate file-system images with realistic metadata and content. Impressions is flexible, supporting user-specified constraints on various file-system parameters using a number of statistical techniques to generate consistent images. In this article, we present the design, implementation, and evaluation of Impressions and demonstrate its utility using desktop search as a case study. We believe Impressions will prove to be useful to system developers and users alike.
Smart phone based robotic arm control using raspberry pi, android and Wi-Fi

This paper proposes a method for controlling a Robotic arm using an application build in the android platform. The android phone and raspberry piboard is connected through Wi-Fi. As the name suggests the robotic arm is designed as it performs the same activity as a human hand works. A signal is generated from the android app which will be received by the raspberry pi board and the robotic arm works according to the predefined program. The android application is the command centre of the robotic arm. The program is written in the python language in the raspberry board. the different data will control the arm rotation.
Interorganizational Collaboration and the Locus of Innovation : Networks of Learning in Biotechnology

Interorganizational Collaboration and the Locus of  Networks of Learning in Biotechnology Author(s): Walter W. Powell, Kenneth W. Koput and Laurel Smith-), pp. 116-145 Published by: on behalf of the Sage Publications, Inc. Johnson Graduate School of Management, Cornell University Stable URL: http://www.jstor.org/stable/2393988 Accessed: 18-12-2015 16:50
Designing a Robot Cognitive Architecture with Concurrency and Active Perception

We are implementing ADAPT, a cognitive architecture for a Pioneer mobile robot, to give the robot the full range of cognitive abilities including perception, use of natural language, learning and the ability to solve complex problems. Our perspective is that an architecture based on a unified theory of robot cognition has the best chance of attaining human-level performance. Existing work in cognitive modeling has accomplished much in the construction of such unified cognitive architectures in areas other than robotics; however, there are major respects in which these architectures are inadequate for robot cognition. This paper examines two major inadequacies of current cognitive architectures for robotics: the absence of support for true concurrency and for active
Analysis, Design, and Experimental Results of a Novel Soft-Switching Snubberless Current-Fed Half-Bridge Front-End Converter-Based PV Inverter

This paper proposes a new novel snubberless current-fed half-bridge front-end isolated dc/dc converter-based inverter for photovoltaic applications. It is suitable for grid-tied (utility interface) as well as off-grid (standalone) application based on the mode of control. The proposed converter attains clamping of the device voltage by secondary modulation, thus eliminating the need of snubber or active-clamp. Zero-current switching or natural commutation of primary devices and zero-voltage switching of secondary devices is achieved. Soft-switching is inherent owing to the proposed secondary modulation and is maintained during wide variation in voltage and power transfer capacity and thus is suitable for photovoltaic (PV) applications. Primary device voltage is clamped at reflected output voltage, and secondary device voltage is clamped at output voltage. Steady-state operation and analysis, and design procedure are presented. Simulation results using PSIM 9.0 are given to verify the proposed analysis and design. An experimental converter prototype rated at 200 W has been designed, built, and tested in the laboratory to verify and demonstrate the converter performance over wide variations in input voltage and output power for PV applications. The proposed converter is a true isolated boost converter and has higher voltage conversion (boost) ratio compared to the conventional active-clamped converter.
A survey of AUV and robot simulators for multi-vehicle operations

This paper presents a survey of a selection of currently available simulation software for robots and unmanned vehicles. In particular, the simulators selected are reviewed for their suitability for the simulation of Autonomous Underwater Vehicles (AUVs), as well as their suitability for the simulation of multi-vehicle operations. The criteria for selection are based on the following features: sufficient physical fidelity to allow modelling of manipulators and end effectors; a programmatic interface, via scripting or middleware; modelling of optical and/or acoustic sensors; adequate documentation; previous use in academic research. A subset of the selected simulators are reviewed in greater detail; these are UWSim, MORSE, and Gazebo. This subset of simulators allow virtual sensors to be simulated, such as GPS, sonar, and multibeam sonar making them suitable for the design and simulation of navigation and mission planning algorithms. We conclude that simulation for underwater vehicles remains a niche problem, but with some additional effort researchers wishing to simulate such vehicles may do so, basing their work on existing software.
Correlated Cascades: Compete or Cooperate

In real world social networks, there are multiple cascades which are rarely independent. They usually compete or cooperate with each other. Motivated by the reinforcement theory in sociology we leverage the fact that adoption of a user to any behavior is modeled by the aggregation of behaviors of its neighbors. We use a multidimensional marked Hawkes process to model users product adoption and consequently spread of cascades in social networks. The resulting inference problem is proved to be convex and is solved in parallel by using the barrier method. The advantage of the proposed model is twofold; it models correlated cascades and also learns the latent diffusion network. Experimental results on synthetic and two real datasets gathered from Twitter, URL shortening and music streaming services, illustrate the superior performance of the proposed model over the alternatives.  Social networks and virtual communities play a key role in today’s life. People share their thoughts, beliefs, opinions, news, and even their locations in social networks and engage in social interactions by commenting, liking, mentioning and following each other. This virtual world is an ideal place for studying social behaviors and spread of cultural norms (Vespignani 2012), contagion of diseases (Barabasi 2015), advertising and marketing (Valera and Rodriguez 2015) and estimating the culprit in malicious diffusions (Farajtabar et al. 2015a). Among them, the study of information diffusion or more generally dynamics on the network is of crucial importance and can be used in many applications. The trace of information diffusion, virus or infection spread, rumor propagation, and product adoption is usually called cascades. In conventional studies of diffusion networks, individual cascades are mostly considered in isolation, i.e., independent of each other (Rodriguez et al. 2015). However in realistic situations, they are rarely independent and can be competitive, when a URL shortening service become popular the others receive less attention; or cooperative, when usage of Google Play Music correlates with that of Youtube due to, for example, simultaneous arrival of new albums . Modeling multiple cascades which are correlated to each other is a challenging problem. Considerable work have  Time (hr) 0 50
New Wilkinson Power Dividers and Their Integration Applications to Four-Way and Filtering Dividers

This paper involved developing two (Type I and Type II) equal-split Wilkinson power dividers (WPDs). The Type I divider can use two short uniform-impedance transmission lines, one resistor, one capacitor, and two quarter-wavelength (λ/4) transformers in its circuit. Compared with the conventional equal-split WPD, the proposed Type I divider can relax the two λ/4 transformers and the output ports layout restrictions of the conventional WPD. To eliminate the number of impedance transformers, the proposed Type II divider requires only one impedance transformer attaining the optimal matching design and a compact size. A compact four-way equal-split WPD based on the proposed Type I and Type II dividers was also developed, facilitating a simple layout, and reducing the circuit size. Regarding the divider, to obtain favorable selectivity and isolation performance levels, two Butterworth filter transformers were integrated in the proposed Type I divider to perform filter response and power split functions. Finally, a single Butterworth filter transformer was integrated in the proposed Type II divider to demonstrate a compact filtering WPD.
Predicting and Characterising User Impact on Twitter

The open structure of online social networks and their uncurated nature give rise to problems of user credibility and influence. In this paper, we address the task of predicting the impact of Twitter users based only on features under their direct control, such as usage statistics and the text posted in their tweets. We approach the problem as regression and apply linear as well as nonlinear learning methods to predict a user impact score, estimated by combining the numbers of the user’s followers, followees and listings. The experimental results point out that a strong prediction performance is achieved, especially for models based on the Gaussian Processes framework. Hence, we can interpret various modelling components, transforming them into indirect ‘suggestions’ for impact boosting.
Deep Stereo: Learning to Predict New Views from the World's Imagery

Deep networks have recently enjoyed enormous success when applied to recognition and classification problems in computer vision [22, 33], but their use in graphics problems has been limited ([23, 7] are notable recent exceptions). In this work, we present a novel deep architecture that performs new view synthesis directly from pixels, trained from a large number of posed image sets. In contrast to traditional approaches, which consist of multiple complex stages of processing, each of which requires careful tuning and can fail in unexpected ways, our system is trained end-to-end. The pixels from neighboring views of a scene are presented to the network, which then directly produces the pixels of the unseen view. The benefits of our approach include generality (we only require posed image sets and can easily apply our method to different domains), and high quality results on traditionally difficult scenes. We believe this is due to the end-to-end nature of our system, which is able to plausibly generate pixels according to color, depth, and texture priors learnt automatically from the training data. We show view interpolation results on imagery from the KITTI dataset , from data from  as well as on Google Street View images. To our knowledge, our work is the first to apply deep learning to the problem of new view synthesis from sets of real-world, natural imagery.
Understanding Personality through Social Media

In this paper, we study the relationship between language use on Twitter and personality traits. Specifically, we want to know how various linguistic features correlate with each personality trait and to what extent can we predict personality traits from language. We gather personality data from Myers-Briggs Type Indicator (MBTI) personality test which contains thinking, feeling, sensation, intuition, introversion, extroversion, judging and perceiving. Using the 90K users in our dataset, we collect most recent tweets from them and design three categories of feature, namely bag of n-grams, Twitter POS tags, and word vectors to explore the most related linguistic features for different personality traits. Analysis of these features provide insights of language use for different personalities. For instances, extroverts tend to use hashtag and phrases like ”so proud”, ”so excited”, and ”can’t wait”. People who like to use emoticon are more likely to be Sensing and Feeling personality type. Moreover, we investigate the predictive power of individual features and combined features in our analysis. With the concatenation of all the features we extracted, we can predict the personality traits with an average AUC of 0.661.