nils-herrmann/clean_scirepeval huggingface dataset:
Automatic fruit recognition and counting from multiple images

In our post-genomic world, where we are deluged with genetic information, the bottleneck to scientific progress is often phenotyping, i.e. measuring the observable characteristics of living organisms, such as counting the number of fruits on a plant. Image analysis is one route to automation. In this paper we present a method for recognising and counting fruits from images in cluttered greenhouses. The plants are 3-metre high peppers with fruits of complex shapes and varying colours similar to the plant canopy. Our calibration and validation datasets each consist of over 28,000 colour images of over 1000 experimental plants. We describe a new twostep method to locate and count pepper fruits: the first step is to find fruits in a single image using a bag-of-words model, and the second is to aggregate estimates from multiple images using a novel statistical approach to cluster repeated, incomplete observations. We demonstrates that image analysis can potentially yield a good correlation with manual measurement (94.6 %) and our proposed method achieves a correlation of 74.2% without any linear adjustment for a large dataset.
DRAPE: DRessing Any PErson

We describe a complete system for animating realistic clothing on synthetic bodies of any shape and pose without manual intervention. The key component of the method is a model of clothing called DRAPE (DRessing Any PErson) that is learned from a physics-based simulation of clothing on bodies of different shapes and poses. The DRAPE model has the desirable property of "factoring" clothing deformations due to body shape from those due to pose variation. This factorization provides an approximation to the physical clothing deformation and greatly simplifies clothing synthesis. Given a parameterized model of the human body with known shape and pose parameters, we describe an algorithm that dresses the body with a garment that is customized to fit and possesses realistic wrinkles. DRAPE can be used to dress static bodies or animated sequences with a learned model of the cloth dynamics. Since the method is fully automated, it is appropriate for dressing large numbers of virtual characters of varying shape. The method is significantly more efficient than physical simulation.
Electrical battery model for use in dynamic electric vehicle simulations

Simulation of electric vehicles, hybrid electric vehicles, and plug-in hybrid electric vehicles over driving schedules within a full dynamic hybrid and electric vehicle simulator requires battery models capable of predicting state-of-charge, I-V characteristics, and dynamic behavior of different battery types. A battery model capable of reproducing lithium-ion, nickel-metal hydride, and lead- acid I-V characteristics (with minimal model alterations) is proposed. A battery-testing apparatus was designed to measure the proposed parameters of the battery model for all three battery types and simulate driving schedules with a programmed source and load configuration. A multiple time-constant battery model was used for modeling lithium- ion batteries; verification of time constants in the seconds to minutes and hour ranges has been shown in numerous research articles and a time constant in the millisecond range is verified here with experiments. Lack of significant time constants in the millisecond range is validated through direct testing. A modeled capacity-rate effect within the state-of-charge determination portion of the proposed model is verified experimentally to ensure accurate prediction of battery state of charge after lengthy driving schedules. The battery model was programmed into a Matlab/Simulink environment and used as a power source for plug-in hybrid electric vehicle simulations. Results from simulations of lithium-ion battery packs show that the proposed battery model behaves well with the other subcomponents of the vehicle simulator; accuracy of the model and prediction of battery internal losses depends on the extent of tests performed on the battery used for the simulation. Extraction of model parameters and their dependence on temperature and cycle number is ongoing, as well as validation of the Simulink model with hardware- in-the-loop "driving schedule" cycling of real batteries.
Client-Centred Therapy

Our thinking in this area has been influenced by the developments in therapeutic thinking in general, but particularly by the evolution of client-centered therapy. The client-centered orientation was, at first, characterized by a rather specific technique or method: the therapist consistently responded by “reflecting” the client’s feeling. This meant that the therapist avoided diagnosis and deductions about the client, or interpretations of hidden meanings the client did not intend. Instead, the therapist attempted always to state in his own words what he sensed to be the client’s intended message, the client’s perception of his feelings, situation, difficulties, and the like. In this effort the therapist tried to sense not merely what was on the surface but the deeply felt meaning which the client only haltingly approached in what he said. Such responding, when successful, meant that the client’s own presently felt message was more deeply understood and responded to than he would have thought possible.
Temporal multi-hierarchy smoothing for estimating rates of rare events

We consider the problem of estimating rates of rare events obtained through interactions among several categorical variables that are heavy-tailed and hierarchical. In our previous work, we proposed a scalable log-linear model called LMMH (Log-Linear Models for Multiple Hierarchies) that combats data sparsity at granular levels through small sample size corrections that borrow strength from rate estimates at coarser resolutions. This paper extends our previous work in two directions. First, we model excess heterogeneity by fitting local LMMH models to relatively homogeneous subsets of the data. To ensure scalable computation, these subsets are induced through a decision tree, we call this Treed-LMMH. Second, the Treed-LMMH method is coupled with temporal smoothing procedure based on a fast Kalman filter style algorithm. We show that simultaneously performing hierarchical and temporal smoothing leads to significant improvement in predictive accuracy. Our methods are illustrated on a large scale computational advertising dataset consisting of billions of observations and hundreds of millions of attribute combinations(cells).
Software development governance: A meta-management perspective

Software development governance is a nascent field of research. Establishing how it is framed early, can significantly affect the progress of contributions. This position paper considers the nature and role of governance in organizations and in the software development domain in particular. In contrast to the dominant functional and structural perspectives, an integrated view of governance is proposed as managing the management of a particular domain (that is, a meta-management perspective). Principles are developed and applied to software development governance and illustrated by a case study.
Kalman filtering over a packet-delaying network: A probabilistic approach

In this paper, we consider Kalman filtering over a packet-delaying network. Given the probability distribution of the delay, we can characterize the filter performance via a probabilistic approach. We assume that the estimator maintains a buffer of length D so that at each time k, the estimator is able to retrieve all available data packets up to time k−D+1. Both the cases of sensorwith andwithout necessary computation capability for filter updates are considered. When the sensor has no computation capability, for a givenD, we give lower and upper bounds on the probability forwhich the estimation error covariance is within a prescribed bound. When the sensor has computation capability, we show that the previously derived lower and upper bounds are equal to each other. An approach for determining the minimum buffer length for a required performance in probability is given and an evaluation on the number of expected filter updates is provided. Examples are provided to demonstrate the theory developed in the paper.
Neuro-fuzzy rule generation: survey in soft computing framework

The present article is a novel attempt in providing an exhaustive survey of neuro-fuzzy rule generation algorithms. Rule generation from artificial neural networks is gaining in popularity in recent times due to its capability of providing some insight to the user about the symbolic knowledge embedded within the network. Fuzzy sets are an aid in providing this information in a more human comprehensible or natural form, and can handle uncertainties at various levels. The neuro-fuzzy approach, symbiotically combining the merits of connectionist and fuzzy approaches, constitutes a key component of soft computing at this stage. To date, there has been no detailed and integrated categorization of the various neuro-fuzzy models used for rule generation. We propose to bring these together under a unified soft computing framework. Moreover, we include both rule extraction and rule refinement in the broader perspective of rule generation. Rules learned and generated for fuzzy reasoning and fuzzy control are also considered from this wider viewpoint. Models are grouped on the basis of their level of neuro-fuzzy synthesis. Use of other soft computing tools like genetic algorithms and rough sets are emphasized. Rule generation from fuzzy knowledge-based networks, which initially encode some crude domain knowledge, are found to result in more refined rules. Finally, real-life application to medical diagnosis is provided.
ERP in action - Challenges and benefits for management control in SME context

January 2011 Received in revised form 7 February 2012 Accepted 2 March 2012 ERP systems have fundamentally re-shaped the way business data is collected, stored, disseminated and used throughout the world. However, the existing research in accounting has provided only relatively few empirical findings on the implications for management control when companies implement ERP systems as the technological platform. Especially scarce are the findings concerning the production phase, after implementation, when the information processes, related work practices and the new information contents can be seen as established. In this paper we explored and theorized the benefits, challenges and problems for management control when an ERP system is in use, four years after the implementation. Our findings also illustrate why and under what circumstances these challenges and benefits may exist. For a holistic view of the organization our findings, based on a qualitative case study, are constructed from the viewpoints of people at different levels and functions of the organization. Top management expected a new strategic control system, but due to the many challenges it ended up with merely financial accounting based control. At the operational level, serious challenges lead to inadequate usage of the ERP system. Management control produces the financial basic data and must contend with many practical problems caused by ERP implementation.
Name-face association with web facial image supervision

This paper describes methods for automatically associating faces detected from multimedia documents with their names presented in the surrounding metadata. We consider the task in the image matching (IM) framework, where external Web facial images are automatically retrieved as the gallery face set of the names in advance, and a detected face is assigned to one of the names, or none of them, according to the association score between the two kinds of faces and constraints. Several important issues are investigated within the IM framework. In collecting Web facial images, beyond the basic scheme that use a celebrity name purely as the query to crawl facial images, a context-assisted image search method is proposed to enhance the relevance and discriminability of the retrieved faces. In constraint formulation, we propose an assigning-thresholding (AT) pipeline to uniformly ensure that the name-face correspondence is strictly one-to-one, and set low confidence associations as null assignments. In association score computation, we propose methods that jointly consider IM with the well-established graph-based association (GA) method at different stages, aiming at producing more accurate scores to benefit the association. Based on these efforts, an Accu-IM method performing the association as accurate as possible and a Fast-IM method performing the association in real-time are respective proposed. Extensive experiments on datasets of captioned News images and Web videos both demonstrate the advantages of the proposed efforts individually and jointly, which consistently provide improvement gains under different settings when compared with state-of-the-art methods.
Narratives: A visualization to track narrative events as they develop

Analyzing unstructured text streams can be challenging. One popular approach is to isolate specific themes in the text, and to visualize the connections between them. Some existing systems, like ThemeRiver, provide a temporal view of changes in themes; other systems, like In-Spire, use clustering techniques to help an analyst identify the themes at a single point in time. Narratives combines both of these techniques; it uses a temporal axis to visualize ways that concepts have changed over time, and introduces several methods to explore how those concepts relate to each other. Narratives is designed to help the user place news stories in their historical and social context by understanding how the major topics associated with them have changed over time. Users can relate articles through time by examining the topical keywords that summarize a specific news event. By tracking the attention to a news article in the form of references in social media (such as weblogs), a user discovers both important events and measures the social relevance of these stories.
Loop closure detection for visual SLAM using PCANet features

Loop closure detection benefits simultaneous localization and mapping (SLAM) in building a consistent map of the environment by reducing the accumulate error. Handcrafted features have been successfully used in traditional approaches, whereas in this paper, we show that unsupervised features extracted by deep learning models, can improves the accuracy of loop closure detection. In particular, we employ a cascaded deep network, namely the PCANet, to extract features as image descriptors. We tested the performance of our proposed method on open datasets to compare with traditional approaches. We found that the PCANet features outperform state-of-the-art handcrafted competitors, and are computational efficient to be implemented in practical robotics.
Compact SIW Diplexer With Flexibly Allocated Bandwidths Using Common Dual-Mode Cavities

A realization method for compact diplexers using mixed dual-mode and single-mode resonators is presented based on an advanced coupling scheme. An example with one common dual-mode (TE101 and TE201) cavity and each channel of two single-mode (TE101) cavities is demonstrated using substrate-integrated waveguide technology. The footprint of the diplexer is reduced compared to the traditional ones since the dual-mode cavity acts as a T-junction between the input port and the two channels and provides one pole for each channel filter. In addition, the fractional bandwidths (FBWs) of the two channel filters can be allocated flexibly based on the FBW design graph. A prototype centered at 12 and 14 GHz with respective FBWs of 3.25% and 3.75% is designed for demonstration.
Measuring Readiness for e-Learning: Reflections from an Emerging Country

In order to benefit from e-learning, companies should conduct considerable up-front analysis to assess their readiness. There are a number of instruments in the market that can be used for assessing readiness for elearning. However, almost all of these instruments are developed to be used in countries that have a mature field of human resources development. So, these instruments consist of terms, phrases, and applications that are meaningless for many companies in especially emerging countries where human resources development field has just shown an improvement. This article includes the description of a survey instrument that has been developed to assess e-learning readiness of companies in these kinds of countries and the results of a study that examines organizational readiness of companies for e-learning in Turkey. The study reveals that companies surveyed are overall ready for e-learning but they need to improve need to improve themselves, particularly in the area of human resources, in order to be able to successfully implement e-learning. Although this instrument has been developed according to the cultural characteristics of Turkish companies it can easily be adapted to be used by companies of other emerging countries.
Attributive Concept Descriptions with Complements

Schmidt-SchaulL M. and G. Smolka, Attributive concept descriptions with complements, Artificial Intelligence 48 (1991) 1-26. We investigate the consequences of adding unions and complements to attributive concept descriptions employed in terminological knowledge representation languages. It is shown that deciding coherence and subsumption of such descriptions are PSPACE-complete problems that can be decided with linear space.
Mining advertiser-specific user behavior using adfactors

Consider an online ad campaign run by an advertiser. The ad serving companies that handle such campaigns record users' behavior that leads to impressions of campaign ads, as well as users' responses to such impressions. This is summarized and reported to the advertisers to help them evaluate the performance of their campaigns and make better budget allocation decisions.
 The most popular reporting statistics are the click-through rate and the conversion rate. While these are indicative of the effectiveness of an ad campaign, the advertisers often seek to understand more sophisticated long-term effects of their ads on the brand awareness and the user behavior that leads to the conversion, thus creating a need for the reporting measures that can capture both the duration and the frequency of the pathways to user conversions.
 In this paper, we propose an alternative data mining framework for analyzing user-level advertising data. In the aggregation step, we compress individual user histories into a graph structure, called the adgraph, representing local correlations between ad events. For the reporting step, we introduce several scoring rules, called the adfactors (AF), that can capture global role of ads and ad paths in the adgraph, in particular, the structural correlation between an ad impression and the user conversion. We present scalable local algorithms for computing the adfactors; all algorithms were implemented using the MapReduce programming model and the Pregel framework.
 Using an anonymous user-level dataset of sponsored search campaigns for eight different advertisers, we evaluate our framework with different adgraphs and adfactors in terms of their statistical fit to the data, and show its value for mining the long-term behavioral patterns in the advertising data.
A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks

Sarcasm detection is a key task for many natural language processing tasks. In sentiment analysis, for example, sarcasm can flip the polarity of an “apparently positive” sentence and, hence, negatively affect polarity detection performance. To date, most approaches to sarcasm detection have treated the task primarily as a text categorization problem. Sarcasm, however, can be expressed in very subtle ways and requires a deeper understanding of natural language that standard text categorization techniques cannot grasp. In this work, we develop models based on a pre-trained convolutional neural network for extracting sentiment, emotion and personality features for sarcasm detection. Such features, along with the network’s baseline features, allow the proposed models to outperform the state of the art on benchmark datasets. We also address the often ignored generalizability issue of classifying data that have not been seen by the models at learning phase.
Modulated Policy Hierarchies

Solving tasks with sparse rewards is a main challenge in reinforcement learning. While hierarchical controllers are an intuitive approach to this problem, current methods often require manual reward shaping, alternating training phases, or manually defined sub tasks. We introduce modulated policy hierarchies (MPH), that can learn end-to-end to solve tasks from sparse rewards. To achieve this, we study different modulation signals and exploration for hierarchical controllers. Specifically, we find that communicating via bit-vectors is more efficient than selecting one out of multiple skills, as it enables mixing between them. To facilitate exploration, MPH uses its different time scales for temporally extended intrinsic motivation at each level of the hierarchy. We evaluate MPH on the robotics tasks of pushing and sparse block stacking, where it outperforms recent baselines.
An Efficient Deep Learning Technique for the Navier-Stokes Equations : Application to Unsteady Wake Flow Dynamics

We present an efficient deep learning technique for the model reduction of the Navier-Stokes equations for unsteady flow problems. The proposed technique relies on the Convolutional Neural Network (CNN) and the stochastic gradient descent method. Of particular interest is to predict the unsteady fluid forces for different bluff body shapes at low Reynolds number. The discrete convolution process with a nonlinear rectification is employed to approximate the mapping between the bluff-body shape and the fluid forces. The deep neural network is fed by the Euclidean distance function as the input and the target data generated by the full-order Navier-Stokes computations for primitive bluff body shapes. The convolutional networks are iteratively trained using the stochastic gradient descent method with the momentum term to predict the fluid force coefficients of different geometries and the results are compared with the full-order computations. We attempt to provide a physical analogy of the stochastic gradient method with the momentum term with the simplified form of the incompressible Navier-Stokes momentum equation. We also construct a direct relationship between the CNN-based deep learning and the Mori-Zwanzig formalism for the model reduction of a fluid dynamical system. A systematic convergence and sensitivity study is performed to identify the effective dimensions of the deeplearned CNN process such as the convolution kernel size, the number of kernels ∗Corresponding author Email address: mperkj@nus.edu (R. K. Jaiman)  2018 ar X iv :1 71 0. 09 09 9v 3 [ ph ys ic s. fl udy n] 1 5 A ug 2 01 8 and the convolution layers. Within the error threshold, the prediction based on our deep convolutional network has a speed-up nearly four orders of magnitude compared to the full-order results and consumes an insignificant fraction of computational resources. The proposed CNN-based approximation procedure has a profound impact on the parametric design of bluff bodies and the feedback control of separated flows.