nils-herrmann/clean_scirepeval huggingface dataset:
Calibrating rhythm: First language and second language studies

This paper presents a comparative evaluation of metrics for the quantification of speech rhythm, comparing pairwise variability indices (nPVI-V and rPVI-C) and interval measures (DV, DC, %V), together with rate-normalised interval measures (VarcoV and VarcoC). First, we examined how well these metrics discriminated ‘‘stress-timed’’ English and Dutch and ‘‘syllable-timed’’ Spanish and French. Metrics of interval standard deviation such as DV and DC were strongly influenced by speech rate, but rate-normalised metrics of vocalic interval variation, VarcoV and nPVI-V, were shown to discriminate between hypothesised ‘‘rhythm classes’’, as did %V, an index of the relative duration of vocalic and consonantal intervals. Second, we applied these metrics to quantifying the influence of first language on second language rhythm, with the expectation that speakers switching ‘‘rhythm classes’’ should show rhythm scores different from both their native and target languages. VarcoV offered the most discriminative analysis in this part of the study, with %V also suggesting insights into the process of accommodation to second language rhythm.
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence

The purpose of this paper is to provide the reader with an extensive technical analysis and review of the book, "Multi agent Systems: A Modern Approach to Distributed Artificial Intelligence" by Gerhard Weiss. Due to the complex nature of the topic of distributed artificial intelligence (DAT) and multi agent systems (MAS), this paper has been divided into two major segments: an overview of field and book analysis. The first section of the paper provides the reader with background information about the topic of DAT and MAS, which not only introduces the reader to the field but also assists the reader to comprehend the essential themes in such a complex field. On the other hand, the second portion of the paper provides the reader with a comprehensive review of the book from the viewpoint of a senior computer science student with an introductory knowledge of the field of artificial intelligence.
TRENDS IN THEORY BUILDING AND THEORY TESTING : A FIVE-DECADE STUDY OF THE ACADEMY OF MANAGEMENT JOURNAL

We introduce a taxonomy that reflects the theoretical contribution of empirical articles along two dimensions: theory building and theory testing. We used that taxonomy to track trends in the theoretical contributions offered by articles over the past five decades. Results based on data from a sample of 74 issues of the Academy of Management Journal reveal upward trends in theory building and testing over time. In addition, the levels of theory building and testing within articles are significant predictors of citation rates. In particular, articles rated moderate to high on both dimensions enjoyed the highest levels of citations.
Data envelopment analysis with missing values: An interval DEA approach

Missing values in inputs, outputs cannot be handled by the original data envelopment analysis (DEA) models. In this paper we introduce an approach based on interval DEA that allows the evaluation of the units with missing values along with the other units with available crisp data. The missing values are replaced by intervals in which the unknown values are likely to belong. The constant bounds of the intervals, depending on the application, can be estimated by using statistical or experiential techniques. For the units with missing values, the proposed models are able to identify an upper and a lower bound of their efficiency scores. The efficiency analysis is further extended by estimating new values for the initial interval bounds that may turn the unit to an efficient one. The proposed methodology is illustrated by an application which evaluates the efficiency of a set of secondary public schools in Greece, a number of which appears to have missing values in some inputs and outputs.
A survey on abstractive summarization techniques

Text Summarization solves climacteric problems in furnishing information to the necessities of user. Due to explosive growth of digital data on internet, information floods are the results to the user queries. This makes user impractical to read entire documents and select the desirables. To this problem summarization is a novel approach which surrogates the original document by not deviating from the theme helps the user to find documents easily. Summarization area was broadly spread over different research fields, Natural Language Processing (NLP), Machine Learning and Semantics etc… Summarization is classified mainly into two techniques Abstract and Extract. This article gives a deep review of Abstract summarization techniques.
Being Responsive to Your Customer: Developing Customer Agility through Information Management

As e-commerce offers new channels for companies to reach consumers, it also brings about significant challenges to the ability to response to customer changing needs, which is named “customer agility”. Both practitioners and researchers consider information management as a source of customer agility. Drawing on the information management literature, this research program attempts to propose an integrative information management framework to study the achievement of customer agility. Subsequently, a process model of how information management helps firms achieve customer agility will be developed. This is achieved by conducting a case study of a Chinese B2C company. This research in progress article presents the preliminary findings from the first visit to the company. It shows that customer agility is achieved through establishing information management structure, developing information management capability and instilling information behaviors and values. More interviews will be conducted in the next phase and the findings will be expanded.
Pareto-based Multi-Objective Differential Evolution

 Evolutionary multi-objective optimization (EMOO) finds a set of Pareto solutions rather than any single aggregated optimal solution for a multi-objective problem. The purpose of this paper is to describe a newly developed evolutionary approach --Paretobased multi-objective differential evolution (MODE). In this paper, the concept of differential evolution, which is well-known in the continuous single-objective domain for its fast convergence and adaptive parameter setting, is extended to the multi-objective problem domain. A Pareto-based approach is proposed to implement the differential vectors. A set of benchmark test functions is used to validate this new approach. We compare the computational results with those obtained in the literature, specifically by strength Pareto evolutionary algorithm (SPEA). It is shown that this new approach tends to be more effective in finding the Pareto front in the sense of accuracy and approximate representation of the real Pareto front with comparable efficiency.
Enhancing Cortana User Experience Using Machine Learning

Voice enabled personal assistants like Microsoft Cortana are becoming better every day. As a result more users are relying on such software to accomplish more tasks. While these applications are significantly improving due to great advancements in the underlying technologies, there are still shortcomings in their performance resulting in a class of user queries that such assistants cannot yet handle with satisfactory results. We analyze the data from millions of user queries, and build a machine learning system capable of classifying user queries into two classes; a class of queries that are addressable by Cortana with high user satisfaction, and a class of queries that are not. We then use unsupervised learning to cluster similar queries and assign them to human assistants who can complement Cortana functionality.
Conditional Image Generation with PixelCNN Decoders

This work explores conditional image generation with a new image density model based on the PixelCNN architecture. The model can be conditioned on any vector, including descriptive labels or tags, or latent embeddings created by other networks. When conditioned on class labels from the ImageNet database, the model is able to generate diverse, realistic scenes representing distinct animals, objects, landscapes and structures. When conditioned on an embedding produced by a convolutional network given a single image of an unseen face, it generates a variety of new portraits of the same person with different facial expressions, poses and lighting conditions. We also show that conditional PixelCNN can serve as a powerful decoder in an image autoencoder. Additionally, the gated convolutional layers in the proposed model improve the log-likelihood of PixelCNN to match the state-ofthe-art performance of PixelRNN on ImageNet, with greatly reduced computational cost.
Chapter 5 Synthetic Polymer-Based Nanoparticles : Intelligent Drug Delivery Systems

One of the most promising strategies to improve the bioavailability of active pharmaceutical ingredients is based on the association of the drug with colloidal carriers, for example, polymeric nanoparticles, which are stable in biological environment, protective for encapsulated substances and able to modulate physicochemical characteristics, drug release and biological behaviour. The synthetic polymers possess unique properties due to their chemical structure. Some of them are characterized with mucoadhesiveness; another can facilitate the penetration through mucous layers; or to be stimuli responsive, providing controlled drug release at the target organ, tissues or cells; and all of them are biocompatible and versatile. These are suitable vehicles of nucleic acids, oligonucleotides, DNA, peptides and proteins. This chapter aims to look at the ‘hot spots’ in the design of synthetic polymer nanoparticles as an intelligent drug delivery system in terms of biopharmaceutical challenges and in relation to the route of their administration: the non-invasive—oral, transdermal, transmucosal (nasal, buccal/sublingual, vaginal, rectal and ocular) and inhalation routes—and the invasive parenteral route.
Sentiment Analysis of Restaurant Reviews on Yelp with Incremental Learning

Sentiment analysis of customer reviews has a crucial impact on a business's development strategy. Despite the fact that a repository of reviews evolves over time, sentiment analysis often relies on offline solutions where training data is collected before the model is built. If we want to avoid retraining the entire model from time to time, incremental learning becomes the best alternative solution for this task. In this work, we present a variant of online random forests to perform sentiment analysis on customers' reviews. Our model is able to achieve accuracy similar to offline methods and comparable to other online models.
Probabilistic programming

Probabilistic programs are usual functional or imperative programs with two added constructs: (1) the ability to draw values at random from distributions, and (2) the ability to condition values of variables in a program via observations. Models from diverse application areas such as computer vision, coding theory, cryptographic protocols, biology and reliability analysis can be written as probabilistic programs. 
 Probabilistic inference is the problem of computing an explicit representation of the probability distribution implicitly specified by a probabilistic program. Depending on the application, the desired output from inference may vary---we may want to estimate the expected value of some function f with respect to the distribution, or the mode of the distribution, or simply a set of samples drawn from the distribution. 
 In this paper, we describe connections this research area called ``Probabilistic Programming" has with programming languages and software engineering, and this includes language design, and the static and dynamic analysis of programs. We survey current state of the art and speculate on promising directions for future research.
A Test Collection for Research on Depression and Language Use

Several studies in the literature have shown that the words people use are indicative of their psychological states. In particular, depression was found to be associated with distinctive linguistic patterns. In this talk, I will describe our first steps to try to identify as early as possible if the writer is developing a depressive state. I will detail the methodology we have adopted to build and make publicly available a test collection on depression and language use. The resulting corpus includes a series of textual interactions written by different subjects. The new collection not only encourages research on differences in language between depressed and non-depressed individuals, but also on the evolution of the language use of depressed individuals. I will also present the new CLEF lab that we will run next year on this topic (eRisk 2017), that includes a novel depression detection task and the proposal of effectiveness measure to systematically compare early detection algorithms and baseline results.  Ses principaux domaines de recherche sont la recherche d'information, la fouille de textes et les bibliothèques numériques. Il a co-édité 10 livres et publié plus de 160 publications dans ces domaines de recherche. Il a été rédacteur en chef de Information Processing and Management (Elsevier) jusqu'en 2015 et membre du comité de rédaction de plusieurs revues. Ses travaux sur les réseaux sociaux sont particulièrement en phase avec les recherches menées par plusieurs équipes de l'IRIT.
Minimum-Threshold Crowbar for a Fault-Ride-Through Grid-Code-Compliant DFIG Wind Turbine

Doubly fed induction generator (DFIG) technology is the dominant technology in the growing global market for wind power generation, due to the combination of variable-speed operation and a cost-effective partially rated power converter. However, the DFIG is sensitive to dips in supply voltage and without specific protection to “ride-through” grid faults, a DFIG risks damage to its power converter due to overcurrent and/or overvoltage. Conventional converter protection via a sustained period of rotor-crowbar closed circuit leads to poor power output and sustained suppression of the stator voltages. A new minimum-threshold rotor-crowbar method is presented in this paper, improving fault response by reducing crowbar application periods to 11-16 ms, successfully diverting transient overcurrents, and restoring good power control within 45 ms of both fault initiation and clearance, thus enabling the DFIG to meet grid-code fault-ride-through requirements. The new method is experimentally verified and evaluated using a 7.5-kW test facility.
Twin-GAN - Unpaired Cross-Domain Image Translation with Weight-Sharing GANs

We present a framework for translating unlabeled images from one domain into analog images in another domain. We employ a progressively growing skipconnected encoder-generator structure and train it with a GAN loss for realistic output, a cycle consistency loss for maintaining same-domain translation identity, and a semantic consistency loss that encourages the network to keep the input semantic features in the output. We apply our framework on the task of translating face images, and show that it is capable of learning semantic mappings for face images with no supervised one-to-one image mapping.
Life cycle assessment of building materials : Comparative analysis of energy and environmental impacts and evaluation of the eco-ef fi ciency improvement potential

The building industry uses great quantities of raw materials that also involve high energy consumption. Choosing materials with high content in embodied energy entails an initial high level of energy consumption in the building production stage but also determines future energy consumption in order to fulfil heating, ventilation and air conditioning demands. This paper presents the results of an LCA study comparing the most commonly used building materials with some eco-materials using three different impact categories. The aim is to deepen the knowledge of energy and environmental specifications of building materials, analysing their possibilities for improvement and providing guidelines for materials selection in the eco-design of new buildings and rehabilitation of existing buildings. The study proves that the impact of construction products can be significantly reduced by promoting the use of the best techniques available and eco-innovation in production plants, substituting the use of finite natural resources for waste generated in other production processes, preferably available locally. This would stimulate competition between manufacturers to launch more eco-efficient products and encourage the use of the Environmental Product Declarations. This paper has been developed within the framework of the “LoRe-LCA Project” co-financed by the European Commission’s Intelligent Energy for Europe Program and the “PSE CICLOPE Project” co-financed by the Spanish Ministry of Science and Technology and the European Regional Development
Online Camera LiDAR Fusion and Object Detection on Hybrid Data for Autonomous Driving

Environment perception for autonomous driving traditionally uses sensor fusion to combine the object detections from various sensors mounted on the car into a single representation of the environment. Non-calibrated sensors result in artifacts and aberration in the environment model, which makes tasks like free-space detection more challenging. In this study, we improve the LiDAR and camera fusion approach of Levinson and Thrun. We rely on intensity discontinuities and erosion and dilation of the edge image for increased robustness against shadows and visual patterns, which is a recurring problem in point cloud related work. Furthermore, we use a gradientfree optimizer instead of an exhaustive grid search to find the extrinsic calibration. Hence, our fusion pipeline is lightweight and able to run in real-time on a computer in the car. For the detection task, we modify the Faster R-CNN architecture to accommodate hybrid LiDAR-camera data for improved object detection and classification. We test our algorithms on the KITTI data set and locally collected urban scenarios. We also give an outlook on how radar can be added to the fusion pipeline via velocity matching.
A Conceptual Model of Service Innovation: The Case of Academic Libraries

Responding to a 2015 MISQ call for research on service innovation, this study develops a conceptual model of service innovation in higher education academic libraries. Digital technologies have drastically altered the delivery of information services in the past decade, raising questions about critical resources, their interaction with digital technologies, and the value of new services and their measurement. Based on new product development (NPD) and new service development (NSD) processes and the service-dominant logic (SDL) perspective, this research-in-progress presents a conceptual model that theorizes interactions between critical resources and digital technologies in an iterative process for delivery of service innovation in academic libraries. The study also suggests future research paths to confirm, expand, and validate the new service innovation model.
A System Based on Intrinsic Features for Fraudulent Document Detection

Paper documents still represent a large amount of information supports used nowadays and may contain critical data. Even though official documents are secured with techniques such as printed patterns or artwork, paper documents suffer from a lack of security. However, the high availability of cheap scanning and printing hardware allows non-experts to easily create fake documents. As the use of a watermarking system added during the document production step is hardly possible, solutions have to be proposed to distinguish a genuine document from a forged one. In this paper, we present an automatic forgery detection method based on document's intrinsic features at character level. This method is based on the one hand on outlier character detection in a discriminant feature space and on the other hand on the detection of strictly similar characters. Therefore, a feature set is computed for all characters. Then, based on a distance between characters of the same class, the character is classified as a genuine one or a fake one.
Data-Driven News Generation for Automated Journalism

Despite increasing amounts of data and ever improving natural language generation techniques, work on automated journalism is still relatively scarce. In this paper, we explore the field and challenges associated with building a journalistic natural language generation system. We present a set of requirements that should guide system design, including transparency, accuracy, modifiability and transferability. Guided by the requirements, we present a data-driven architecture for automated journalism that is largely domain and language independent. We illustrate its practical application in the production of news articles upon a user request about the 2017 Finnish municipal elections in three languages, demonstrating the successfulness of the data-driven, modular approach of the design. We then draw some lessons for future automated journalism.
Factoring Polynomials Over Finite Fields: A Survey

Finding the factorization of a polynomial over a finite field is of interest not only independently but also for many applications in computer algebra, algebraic coding theory, cryptography, and computational number theory. Polynomial factorization over finite fields is used as a subproblem in algorithms for factoring polynomials over the integers (Zassenhaus, 1969; Collins, 1979; Lenstra et al., 1982; Knuth, 1998), for constructing cyclic redundancy codes and BCH codes (Berlekamp, 1968; MacWilliams and Sloane, 1977; van Lint, 1982), for designing public key cryptosystems (Chor and Rivest, 1985; Odlyzko, 1985; Lenstra, 1991), and for computing the number of points on elliptic curves (Buchmann, 1990). Major improvements have been made in the polynomial factorization problem during this decade both in theory and in practice. From a theoretical point of view, asymptotically faster algorithms have been proposed. However, these advances are yet more striking in practice where variants of the asymptotically fastest algorithms allow us to factor polynomials over finite fields in reasonable amounts of time that were unassailable a few years ago. Our purpose in this survey is to stress the basic ideas behind these methods, to overview experimental results, as well as to give a comprehensive up-to-date bibliography of the problem. Kaltofen (1982, 1990, 1992) has given excellent surveys of
Multi-clue fusion for emotion recognition in the wild

In the past three years, Emotion Recognition in the Wild (EmotiW) Grand Challenge has drawn more and more attention due to its huge potential applications. In the fourth challenge, aimed at the task of video based emotion recognition, we propose a multi-clue emotion fusion (MCEF) framework by modeling human emotion from three mutually complementary sources, facial appearance texture, facial action, and audio. To extract high-level emotion features from sequential face images, we employ a CNN-RNN architecture, where face image from each frame is first fed into the fine-tuned VGG-Face network to extract face feature, and then the features of all frames are sequentially traversed in a bidirectional RNN so as to capture dynamic changes of facial textures. To attain more accurate facial actions, a facial landmark trajectory model is proposed to explicitly learn emotion variations of facial components. Further, audio signals are also modeled in a CNN framework by extracting low-level energy features from segmented audio clips and then stacking them as an image-like map. Finally, we fuse the results generated from three clues to boost the performance of emotion recognition. Our proposed MCEF achieves an overall accuracy of 56.66% with a large improvement of 16.19% with respect to the baseline.