nils-herrmann/clean_scirepeval huggingface dataset:
Portrait of an Online Shopper: Understanding and Predicting Consumer Behavior

Consumer spending accounts for a large fraction of economic footprint of modern countries. Increasingly, consumer activity is moving to the web, where digital receipts of online purchases provide valuable data sources detailing consumer behavior. We consider such data extracted from emails and combined with with consumers' demographic information, which we use to characterize, model, and predict purchasing behavior. We analyze such behavior of consumers in different age and gender groups, and find interesting, actionable patterns that can be used to improve ad targeting systems. For example, we found that the amount of money spent on online purchases grows sharply with age, peaking in the late 30s, while shoppers from wealthy areas tend to purchase more expensive items and buy them more frequently. Furthermore, we look at the influence of social connections on purchasing habits, as well as at the temporal dynamics of online shopping where we discovered daily and weekly behavioral patterns. Finally, we build a model to predict when shoppers are most likely to make a purchase and how much will they spend, showing improvement over baseline approaches. The presented results paint a clear picture of a modern online shopper, and allow better understanding of consumer behavior that can help improve marketing efforts and make shopping more pleasant and efficient experience for online customers.
Does Life Satisfaction influence the intention (We-Intention) to use Facebook?

Several studies have investigated a variety of factors affecting use of social networking sites (SNSs), but the investigation of these factors is still under development. In this study, we aim to contribute to the literature by extending and testing an existent conceptual model in a new context, including Life Satisfaction as an independent variable to explain the intention (We-Intention) to use Facebook. The previous models has Subjective Norm, Group Norms, Social Identity, Purposive Value, Self-Discovery, Maintaining Interpersonal Interconnectivity, Social Enhancement, Entertainment Value and Social Presence as the predictor variables. An online survey with Brazilians (n = 1111) was conducted. Our structural equation modeling reveals that Life Satisfaction influence on We-Intention is mediated by Subjective Norm, Group Norm, Social Identity, Entertainment Value, and Maintaining Interpersonal Interconnectivity (R-squared value is 0.36). Our findings, while consistent with existing literature in terms of theories, reveal different arrangements among factors influencing Brazilian consumers’ behavior.
Computing Machinery and Intelligence A.M. Turing

I propose to consider the question, "Can machines think?" This should begin with definitions of the meaning of the terms "machine" and "think." The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous, If the meaning of the words "machine" and "think" are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, "Can machines think?" is to be sought in a statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words.
A Tandem Algorithm for Singing Pitch Extraction and Voice Separation From Music Accompaniment

Singing pitch estimation and singing voice separation are challenging due to the presence of music accompaniments that are often nonstationary and harmonic. Inspired by computational auditory scene analysis (CASA), this paper investigates a tandem algorithm that estimates the singing pitch and separates the singing voice jointly and iteratively. Rough pitches are first estimated and then used to separate the target singer by considering harmonicity and temporal continuity. The separated singing voice and estimated pitches are used to improve each other iteratively. To enhance the performance of the tandem algorithm for dealing with musical recordings, we propose a trend estimation algorithm to detect the pitch ranges of a singing voice in each time frame. The detected trend substantially reduces the difficulty of singing pitch detection by removing a large number of wrong pitch candidates either produced by musical instruments or the overtones of the singing voice. Systematic evaluation shows that the tandem algorithm outperforms previous systems for pitch extraction and singing voice separation.
Anonymity, Unobservability, and Pseudonymity - A Proposal for Terminology

Based on the nomenclature of the early papers in the field, we propose a set of terminology which is both expressive and precise. More particularly, we define anonymity, unlinkability, unobservability, and pseudonymity (pseudonyms and digital pseudonyms, and their attributes). We hope that the adoption of this terminology might help to achieve better progress in the field by avoiding that each researcher invents a language of his/her own from scratch. Of course, each paper will need additional vocabulary, which might be added consistently to the terms defined here.
Jointly Extracting Event Triggers and Arguments by Dependency-Bridge RNN and Tensor-Based Argument Interaction

Event extraction plays an important role in natural language processing (NLP) applications including question answering and information retrieval. Traditional event extraction relies heavily on lexical and syntactic features, which require intensive human engineering and may not generalize to different datasets. Deep neural networks, on the other hand, are able to automatically learn underlying features, but existing networks do not make full use of syntactic relations. In this paper, we propose a novel dependency bridge recurrent neural network (dbRNN) for event extraction. We build our model upon a recurrent neural network, but enhance it with dependency bridges, which carry syntactically related information when modeling each word. We illustrates that simultaneously applying tree structure and sequence structure in RNN brings much better performance than only uses sequential RNN. In addition, we use a tensor layer to simultaneously capture the various types of latent interaction between candidate arguments as well as identify/classify all arguments of an event. Experiments show that our approach achieves competitive results compared with previous work.
Geographic and Genetic Population Differentiation of the Amazonian Chocolate Tree (Theobroma cacao L)

Numerous collecting expeditions of Theobroma cacao L. germplasm have been undertaken in Latin-America. However, most of this germplasm has not contributed to cacao improvement because its relationship to cultivated selections was poorly understood. Germplasm labeling errors have impeded breeding and confounded the interpretation of diversity analyses. To improve the understanding of the origin, classification, and population differentiation within the species, 1241 accessions covering a large geographic sampling were genotyped with 106 microsatellite markers. After discarding mislabeled samples, 10 genetic clusters, as opposed to the two genetic groups traditionally recognized within T. cacao, were found by applying Bayesian statistics. This leads us to propose a new classification of the cacao germplasm that will enhance its management. The results also provide new insights into the diversification of Amazon species in general, with the pattern of differentiation of the populations studied supporting the palaeoarches hypothesis of species diversification. The origin of the traditional cacao cultivars is also enlightened in this study.
The Kaldi Speech Recognition Toolkit

We describe the design of Kaldi, a free, open-source toolkit for speech recognition research. Kaldi provides a speech recognition system based on finite-state transducers (using the freely available OpenFst), together with detailed documentation and scripts for building complete recognition systems. Kaldi is written is C++, and the core library supports modeling of arbitrary phonetic-context sizes, acoustic modeling with subspace Gaussian mixture models (SGMM) as well as standard Gaussian mixture models, together with all commonly used linear and affine transforms. Kaldi is released under the Apache License v2.0, which is highly nonrestrictive, making it suitable for a wide community of users.
Bregman Voronoi Diagrams

The Voronoi diagram of a point set is a fundamental geometric structure that partitions the space into elementary regions of influence defining a discrete proximity graph and dually a well-shaped Delaunay triangulation. In this paper, we investigate a framework for defining and building the Voronoi diagrams for a broad class of distortion measures called Bregman divergences, that includes not only the traditional (squared) Euclidean distance, but also various divergence measures based on entropic functions. As a by-product, Bregman Voronoi diagrams allow one to define information-theoretic Voronoi diagrams in statistical parametric spaces based on the relative entropy of distributions. We show that for a given Bregman divergence, one can define several types of Voronoi diagrams related to each other by convex duality or embedding. Moreover, we can always compute them indirectly as power diagrams in primal or dual spaces, or directly after linearization in an extra-dimensional space as the projection of a Euclidean polytope. Finally, our paper proposes to generalize Bregman divergences to higher-order terms, called κ-jet Bregman divergences, and touch upon their Voronoi diagrams.
PSO-based optimization for isolated intersections signal timings and simulation

In this paper, based on the analyzing the signal control problem of single intersections, a kind of real-time optimization method with PSO (particle swarm optimization) solving signal timings for single intersection is put forward. On detecting the flow of current cycle and the cycle before, estimating the flow of next circle is proceeded. Setting least delay as performance index, we get each phase time for the next cycle. A simulation experiment for the traffic model at a four-phase intersection is also performed. The result shows that the method is efficient.
A Bottom-Up Approach to Sentence Ordering for Multi-Document Summarization

Ordering information is a difficult but important task for applications generating naturallanguage texts such as multi-document summarization, question answering, and conceptto-text generation. In multi-document summarization, information is selected from a set of source documents. However, improper ordering of information in a summary can confuse the reader and deteriorate the readability of the summary. Therefore, it is vital to properly order the information in multi-document summarization. We present a bottom-up approach to arrange sentences extracted for multi-document summarization. To capture the association and order of two textual segments (e.g. sentences), we define four criteria: chronology, topical-closeness, precedence, and succession. These criteria are integrated into a criterion by a supervised learning approach. We repeatedly concatenate two textual segments into one segment based on the criterion, until we obtain the overall segment with all sentences arranged. We evaluate the sentence orderings produced by the proposed method and numerous baselines using subjective gradings as well as automatic evaluation measures. We introduce the average continuity, an automatic evaluation measure of sentence ordering in a summary, and investigate its appropriateness for this task.
Interactive Visual Exploration of a Large Spatio-temporal Dataset: Reflections on a Geovisualization Mashup.

Exploratory visual analysis is useful for the preliminary investigation of large structured, multifaceted spatio-temporal datasets. This process requires the selection and aggregation of records by time, space and attribute, the ability to transform data and the flexibility to apply appropriate visual encodings and interactions. We propose an approach inspired by geographical 'mashups' in which freely-available functionality and data are loosely but flexibly combined using de facto exchange standards. Our case study combines MySQL, PHP and the LandSerf GIS to allow Google Earth to be used for visual synthesis and interaction with encodings described in KML. This approach is applied to the exploration of a log of 1.42 million requests made of a mobile directory service. Novel combinations of interaction and visual encoding are developed including spatial 'tag clouds', 'tag maps', 'data dials' and multi-scale density surfaces. Four aspects of the approach are informally evaluated: the visual encodings employed, their success in the visual exploration of the dataset, the specific tools used and the 'mashup' approach. Preliminary findings will be beneficial to others considering using mashups for visualization. The specific techniques developed may be more widely applied to offer insights into the structure of multifarious spatio-temporal data of the type explored here.
Ultra compact filters for ultra-wideband (UWB) applications using multilayer ring resonators

Novel compact ultra-wideband (UWB) bandpass filters are proposed. It is based on multilayer ring resonators (MRR). The ring is applied to any loop shape. The filter has a simple structure for fabrication and the design technique can be adapted in order to realize both narrowband and ultra-wideband filters. With the MRR, filters of bandwidths in excess of 10 GHz while enjoying a wide stop-band can be designed. The fractional bandwidth of more than 143% and a group delay of less than 0.2ns are achieved with this class of filters. Also, in this class of filters, it is easy to place the attenuation poles in order to fit a requirement. Due to its small circuit size (around 2 mm in each dimension), this filter can be used in hybrid integration with small UWB, WLAN and radar devices. Since the filter is located within two ground planes, no radiation affects its characteristics. Two types of the proposed UWB filters were fabricated and the measurement verified the theory and simulation.
Coupling design and performance analysis of rim-driven integrated motor propulsor

Coupling design of rim-driven integrated motor propulsor for vessels and underwater vehicle is presented in this paper. The main characteristic of integrated motor propulsor is that the motor is integrated in the duct of propulsor. So the coupling design of motor and propulsor is the key to the overall design. Considering the influence of the motor and duct size, the propeller was designed, and the CFD Method was used to analyze the hydrodynamic performance of the propulsor. Based on the air-gap magnetic field of permanent magnet motor and the equivalent magnetic circuit, the integrated motor electromagnetic model was proposed, and the finite element method was used to analyze the motor electromagnetic field. Finally, the simulation of the integrated motor starting process with the load of propulsor torque was carried out, and the results meets the design specifications.
Towards Score Following In Sheet Music Images

This paper addresses the matching of short music audio snippets to the corresponding pixel location in images of sheet music. A system is presented that simultaneously learns to read notes, listens to music and matches the currently played music to its corresponding notes in the sheet. It consists of an end-to-end multi-modal convolutional neural network that takes as input images of sheet music and spectrograms of the respective audio snippets. It learns to predict, for a given unseen audio snippet (covering approximately one bar of music), the corresponding position in the respective score line. Our results suggest that with the use of (deep) neural networks – which have proven to be powerful image processing models – working with sheet music becomes feasible and a promising future research direction.
Representation Learning on Graphs with Jumping Knowledge Networks

Recent deep learning approaches for representation learning on graphs follow a neighborhood aggregation procedure. We analyze some important properties of these models, and propose a strategy to overcome those. In particular, the range of “neighboring” nodes that a node’s representation draws from strongly depends on the graph structure, analogous to the spread of a random walk. To adapt to local neighborhood properties and tasks, we explore an architecture – jumping knowledge (JK) networks – that flexibly leverages, for each node, different neighborhood ranges to enable better structure-aware representation. In a number of experiments on social, bioinformatics and citation networks, we demonstrate that our model achieves state-of-the-art performance. Furthermore, combining the JK framework with models like Graph Convolutional Networks, GraphSAGE and Graph Attention Networks consistently improves those models’ performance.
A software reliability model for web services

This paper proposes a service-oriented software reliability model that dynamically evaluates the reliability of Web services. There are two kinds of Web services: atomic services without the structural information and the composite services consisting of atomic services. The model first evaluates the reliability of atomic services based on group testing and majority voting. Group testing is the key technique proposed in this paper to support the service-oriented reliability model. Then, the reliability model evaluates the overall reliability of composite services using an architecture-based model and based on reliabilities of the atomic services, execution scenarios, and operational profiles. The reliability model is dynamic and the reliabilities of the services are evaluated in the actual operational environment. A case study is designed, implemented, and analyzed using the design of experiment technique. The results show the significances of the model and its components.
Connecting modalities: Semi-supervised segmentation and annotation of images using unaligned text corpora

We propose a semi-supervised model which segments and annotates images using very few labeled images and a large unaligned text corpus to relate image regions to text labels. Given photos of a sports event, all that is necessary to provide a pixel-level labeling of objects and background is a set of newspaper articles about this sport and one to five labeled images. Our model is motivated by the observation that words in text corpora share certain context and feature similarities with visual objects. We describe images using visual words, a new region-based representation. The proposed model is based on kernelized canonical correlation analysis which finds a mapping between visual and textual words by projecting them into a latent meaning space. Kernels are derived from context and adjective features inside the respective visual and textual domains. We apply our method to a challenging dataset and rely on articles of the New York Times for textual features. Our model outperforms the state-of-the-art in annotation. In segmentation it compares favorably with other methods that use significantly more labeled training data.
Continuous Deployment at Facebook and OANDA

Continuous deployment is the software engineering practice of deploying many small incremental software updates into production, leading to a continuous stream of 10s, 100s, or even 1,000s of deployments per day. High-profile Internet firms such as Amazon, Etsy, Facebook, Flickr, Google, and Netflix have embraced continuous deployment. However, the practice has not been covered in textbooks and no scientific publication has presented an analysis of continuous deployment.
 In this paper, we describe the continuous deployment practices at two very different firms: Facebook and OANDA. We show that continuous deployment does not inhibit productivity or quality even in the face of substantial engineering team and code size growth. To the best of our knowledge, this is the first study to show it is possible to scale the size of an engineering team by 20X and the size of the code base by 50X without negatively impacting developer productivity or software quality. Our experience suggests that top-level management support of continuous deployment is necessary, and that given a choice, developers prefer faster deployment. We identify elements we feel make continuous deployment viable and present observations from operating in a continuous deployment environment.
Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part I)

An extreme learning machine (ELM) is a feedforward neural network (FNN) like learning system whose connections with output neurons are adjustable, while the connections with and within hidden neurons are randomly fixed. Numerous applications have demonstrated the feasibility and high efficiency of ELM-like systems. It has, however, been open if this is true for any general applications. In this two-part paper, we conduct a comprehensive feasibility analysis of ELM. In Part I, we provide an answer to the question by theoretically justifying the following: 1) for some suitable activation functions, such as polynomials, Nadaraya-Watson and sigmoid functions, the ELM-like systems can attain the theoretical generalization bound of the FNNs with all connections adjusted, i.e., they do not degrade the generalization capability of the FNNs even when the connections with and within hidden neurons are randomly fixed; 2) the number of hidden neurons needed for an ELM-like system to achieve the theoretical bound can be estimated; and 3) whenever the activation function is taken as polynomial, the deduced hidden layer output matrix is of full column-rank, therefore the generalized inverse technique can be efficiently applied to yield the solution of an ELM-like system, and, furthermore, for the nonpolynomial case, the Tikhonov regularization can be applied to guarantee the weak regularity while not sacrificing the generalization capability. In Part II, however, we reveal a different aspect of the feasibility of ELM: there also exists some activation functions, which makes the corresponding ELM degrade the generalization capability. The obtained results underlie the feasibility and efficiency of ELM-like systems, and yield various generalizations and improvements of the systems as well.
Near-optimal Regret Bounds for Reinforcement Learning

For undiscounted reinforcement learning in Markov decision processes (MDPs) we consider the total regret of a learning algorithm with respect to an optimal policy. In order to describe the transition structure of an MDP we propose a new parameter: An MDP has diameter D if for any pair of states s, s′ there is a policy which moves from s to s′ in at most D steps (on average). We present a reinforcement learning algorithm with total regret Õ(DS √ AT ) after T steps for any unknown MDP with S states, A actions per state, and diameter D. This bound holds with high probability. We also present a corresponding lower bound of Ω( √ DSAT ) on the total regret of any learning algorithm.