nils-herrmann/clean_scirepeval huggingface dataset:
Deep Networks with Internal Selective Attention through Feedback Connections

Traditional convolutional neural networks (CNN) are stationary and feedforward. They neither change their parameters during evaluation nor use feedback from higher to lower layers. Real brains, however, do. So does our Deep Attention Selective Network (dasNet) architecture. DasNet’s feedback structure can dynamically alter its convolutional filter sensitivities during classification. It harnesses the power of sequential processing to improve classification performance, by allowing the network to iteratively focus its internal attention on some of its convolutional filters. Feedback is trained through direct policy search in a huge million-dimensional parameter space, through scalable natural evolution strategies (SNES). On the CIFAR-10 and CIFAR-100 datasets, dasNet outperforms the previous state-of-the-art model on unaugmented datasets.
A deep reinforcement learning based model supporting object familiarization

An important ability of cognitive systems is the ability to familiarize themselves with the properties of objects and their environment as well as to develop an understanding of the consequences of their own actions on physical objects. Developing developmental approaches that allow cognitive systems to familiarize with objects in this sense via guided self-exploration is an important challenge within the field of developmental robotics. In this paper we present a novel approach that allows cognitive systems to familiarize themselves with the properties of objects and the effects of their actions on them in a self-exploration fashion. Our approach is inspired by developmental studies that hypothesize that infants have a propensity to systematically explore the connection between own actions and their perceptual consequences in order to support inter-modal calibration of their bodies. We propose a reinforcement-based approach operating in a continuous state space in which the function predicting cumulated future rewards is learned via a deep Q-network. We investigate the impact of the structure of rewards, the impact of different regularization approaches as well as the impact of different exploration strategies.
mrFPGA: A novel FPGA architecture with memristor-based reconfiguration

In this paper, we introduce a novel FPGA architecture with memristor-based reconfiguration (mrFPGA). The proposed architecture is based on the existing CMOS-compatible memristor fabrication process. The programmable interconnects of mrFPGA use only memristors and metal wires so that the interconnects can be fabricated over logic blocks, resulting in significant reduction of overall area and interconnect delay but without using a 3D die-stacking process. Using memristors to build up the interconnects can also provide capacitance shielding from unused routing paths and reduce interconnect delay further. Moreover we propose an improved architecture that allows adaptive buffer insertion in interconnects to achieve more speedup. Compared to the fixed buffer pattern in conventional FPGAs, the positions of inserted buffers in mrFPGA are optimized on demand. A complete CAD flow is provided for mrFPGA, with an advanced P&R tool named mrVPR that was developed for mrFPGA. The tool can deal with the novel routing structure of mrFPGA, the memristor shielding effect, and the algorithm for optimal buffer insertion. We evaluate the area, performance and power consumption of mrFPGA based on the 20 largest MCNC benchmark circuits. Results show that mrFPGA achieves 5.18x area savings, 2.28x speedup and 1.63x power savings. Further improvement is expected with combination of 3D technologies and mrFPGA.
Joint Modeling of Content and Discourse Relations in Dialogues

We present a joint modeling approach to identify salient discussion points in spoken meetings as well as to label the discourse relations between speaker turns. A variation of our model is also discussed when discourse relations are treated as latent variables. Experimental results on two popular meeting corpora show that our joint model can outperform state-of-the-art approaches for both phrasebased content selection and discourse relation prediction tasks. We also evaluate our model on predicting the consistency among team members’ understanding of their group decisions. Classifiers trained with features constructed from our model achieve significant better predictive performance than the state-of-the-art.
Ecosystem Services in Decision Making: Time to Deliver

The vision of the MA – and of the prescient ecologists and economists whose work formed its foundation – is a world in which people and institutions appreciate natural systems as vital assets, recognize the central roles these assets play in supporting human well-being, and routinely incorporate their material and intangible values into decision making. This vision is now beginning to take hold, fueled by innovations from around the world – from pioneering local leaders to government bureaucracies, and from traditional cultures to major corporations (eg a new experimental wing of Goldman Sachs; Daily and Ellison 2002; Bhagwat and Rutte 2006; Kareiva and Marvier 2007; Ostrom et al. 2007; Goldman et al. 2008). China, for instance, is investing over 700 billion yuan (about US$102.6 billion) in ecosystem service payments, in the current decade (Liu et al. 2008). The goal of the Natural Capital Project – a partnership between Stanford University, The Nature Conservancy, and World Wildlife Fund (www.naturalcapitalproject.org) – is to help integrate ecosystem services into everyday decision making around the world. This requires turning the valuation of ecosystem services into effective policy and finance mechanisms – a problem that, as yet, no one has solved on a large scale. A key challenge remains: relative to other forms of capital, assets embodied in ecosystems are often poorly understood, rarely monitored, and are undergoing rapid degradation (Heal 2000a; MA 2005; Mäler et al. 2008). The importance of ecosystem services is often recognized only after they have been lost, as was the case following Hurricane Katrina (Chambers et al. 2007). Natural capital, and the ecosystem services that flow from it, are usually undervalued – by governments, businesses, and the public – if indeed they are considered at all (Daily et al. 2000; Balmford et al. 2002; NRC 2005). Two fundamental changes need to occur in order to replicate, scale up, and sustain the pioneering efforts that are currently underway, to give ecosystem services weight in decision making. First, the science of ecosystem services needs to advance rapidly. In promising a return (of services) on investments in nature, the scientific community needs to deliver the knowledge and tools necessary to forecast and quantify this return. To help address this challenge, the Natural Capital Project has developed InVEST (a system for Integrated Valuation of Ecosystem ECOSYSTEM SERVICES ECOSYSTEM SERVICES ECOSYSTEM SERVICES
Remembering the real me: Nostalgia offers a window to the intrinsic self.

Seven studies reveal that nostalgia, a sentimental affection for the past, offers a window to the intrinsic self-concept-who people think they truly are. In Study 1, state nostalgia was associated with higher authenticity and lower extrinsic self-focus (concern with meeting extrinsic value standards). In Study 2, experimentally primed nostalgia increased perceived authenticity of the past self, which in turn predicted reduced current extrinsic self-focus. Study 3 showed that nostalgia increased the accessibility of the intrinsic self-concept but not the everyday self-concept. Study 4 provided evidence for a moderator suggested by our theoretical analysis: Recalling a nostalgic event increased felt nostalgia and positive affect, but this effect was attenuated if participants were prompted to recognize external factors controlling their behavior during that event. Next we treated nostalgia as an outcome variable and a moderator to test whether nostalgia is triggered by, and buffers against, threats to the intrinsic self. Using a mediation approach, Study 5 showed that participants primed to feel blocked in intrinsic self-expression responded with increased nostalgia. In Study 6, intrinsic self-threat reduced intrinsic self-expression and subjective well-being for participants who were not given an opportunity to respond with nostalgia but not for participants who were allowed to reflect on a nostalgic memory. In line with the experimental findings, correlational data from Study 7 indicated that dispositional nostalgia positively predicted intrinsic self-expression and well-being. Understanding nostalgia as a window to the intrinsic self points to new directions for research on nostalgia's antecedents, moderators, and consequences for well-being.
Sample Compression, Learnability, and the Vapnik-Chervonenkis Dimension

Within the framework of pac-learning, we explore the learnability of concepts from samples using the paradigm of sample compression schemes. A sample compression scheme of size k for a concept class C $$\subseteq $$ 2 X consists of a compression function and a reconstruction function. The compression function receives a finite sample set consistent with some concept in C and chooses a subset of k examples as the compression set. The reconstruction function forms a hypothesis on X from a compression set of k examples. For any sample set of a concept in C the compression set produced by the compression function must lead to a hypothesis consistent with the whole original sample set when it is fed to the reconstruction function. We demonstrate that the existence of a sample compression scheme of fixed-size for a class C is sufficient to ensure that the class C is pac-learnable. Previous work has shown that a class is pac-learnable if and only if the Vapnik-Chervonenkis (VC) dimension of the class is finite. In the second half of this paper we explore the relationship between sample compression schemes and the VC dimension. We define maximum and maximal classes of VC dimension d. For every maximum class of VC dimension d, there is a sample compression scheme of size d, and for sufficiently-large maximum classes there is no sample compression scheme of size less than d. We discuss briefly classes of VC dimension d that are maximal but not maximum. It is an open question whether every class of VC dimension d has a sample compression scheme of size O(d).
STRATEGIC HUMAN RESOURCE MANAGEMENT SYLLABUS ( Fall 2017 )

In this course, you will: • Learn about the HR function, its strategic organizational position, and the challenges faced in developing human resources to ensure retention and growth of core competencies. • Learn from the text, current issues in the news and classroom discussion the practice and management of HR functions as related to job design, staffing, training, compensation, benefits, assessment, counseling, IT, labor relations, legal issues, and organizational culture and change. • Have the opportunity to test the concepts, innovate and contribute from your personal experiences to enrich the class. • Demonstrate your understanding of these concepts through individual and group exercises, papers and projects. These activities will serve as a platform for you to examine the issues, problem-solve and critically analyze the outcomes.
Attracting Whom? - Managing User-Generated-Content Communities for Monetization

Successful monetization of user-generated-content (UGC) business calls for attracting enough users, and the right users. The defining characteristic of UGC is users are also content contributors. In this study, we analyze the impact of a UGC firm’s quality control decision on user community composition. We model two UGC firms in competition, with one permitting only high quality content while the other not controlling quality. Users differ in their valuations and the content quality they contribute. Through analyzing various equilibrium situations, we find that higher reward value generally benefits the firm without quality control. However, when the intrinsic value of contribution is low, higher reward value may surprisingly drive high valuation users away from that firm. Also somewhat interestingly, we find that higher cost of contribution may benefit the firm that does not control quality. Our work is among the first to study the business impact of quality control of UGC.
Credit risk prediction in an imbalanced social lending environment

Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique to use with imbalanced data is still controversial. In an attempt to address these problems, this paper presents an empirical comparison of various combinations of classifiers and resampling techniques within a novel risk assessment methodology that incorporates imbalanced data. The credit predictions from each combination are evaluated with a G-mean measure to avoid bias towards the majority class, which has not been considered in similar studies. The results reveal that combining random forest and random under-sampling may be an effective strategy for calculating the credit risk associated with loan applicants in social lending markets.
Recognizing facial expression: machine learning and application to spontaneous behavior

We present a systematic comparison of machine learning methods applied to the problem of fully automatic recognition of facial expressions. We report results on a series of experiments comparing recognition engines, including AdaBoost, support vector machines, linear discriminant analysis. We also explored feature selection techniques, including the use of AdaBoost for feature selection prior to classification by SVM or LDA. Best results were obtained by selecting a subset of Gabor filters using AdaBoost followed by classification with support vector machines. The system operates in real-time, and obtained 93% correct generalization to novel subjects for a 7-way forced choice on the Cohn-Kanade expression dataset. The outputs of the classifiers change smoothly as a function of time and thus can be used to measure facial expression dynamics. We applied the system to to fully automated recognition of facial actions (FACS). The present system classifies 17 action units, whether they occur singly or in combination with other actions, with a mean accuracy of 94.8%. We present preliminary results for applying this system to spontaneous facial expressions.
Attentive Contexts for Object Detection

Modern deep neural network-based object detection methods typically classify candidate proposals using their interior features. However, global and local surrounding contexts that are believed to be valuable for object detection are not fully exploited by existing methods yet. In this work, we take a step towards understanding what is a robust practice to extract and utilize contextual information to facilitate object detection in practice. Specifically, we consider the following two questions: “how to identify useful global contextual information for detecting a certain object?” and “how to exploit local context surrounding a proposal for better inferring its contents?” We provide preliminary answers to these questions through developing a novel attention to context convolution neural network (AC-CNN)-based object detection model. AC-CNN effectively incorporates global and local contextual information into the region-based CNN (e.g., fast R-CNN and faster R-CNN) detection framework and provides better object detection performance. It consists of one attention-based global contextualized (AGC) subnetwork and one multi-scale local contextualized (MLC) subnetwork. To capture global context, the AGC subnetwork recurrently generates an attention map for an input image to highlight useful global contextual locations, through multiple stacked long short-term memory layers. For capturing surrounding local context, the MLC subnetwork exploits both the inside and outside contextual information of each specific proposal at multiple scales. The global and local context are then fused together for making the final decision for detection. Extensive experiments on PASCAL VOC 2007 and VOC 2012 well demonstrate the superiority of the proposed AC-CNN over well-established baselines.
End-to-End People Detection in Crowded Scenes

Current people detectors operate either by scanning an image in a sliding window fashion or by classifying a discrete set of proposals. We propose a model that is based on decoding an image into a set of people detections. Our system takes an image as input and directly outputs a set of distinct detection hypotheses. Because we generate predictions jointly, common post-processing steps such as nonmaximum suppression are unnecessary. We use a recurrent LSTM layer for sequence generation and train our model end-to-end with a new loss function that operates on sets of detections. We demonstrate the effectiveness of our approach on the challenging task of detecting people in crowded scenes1.
Fraud Detection in Mobile Telecommunication

—This paper presents a predictive model to handle customer insolvency in advance for large mobile telecommunication companies for the purpose of minimizing their losses. However, another goal is of the highest interest for large mobile telecommunication companies is based on maintaining an overall satisfaction of the customers which may have important consequences on the quality and on the consume return of the operations. In this paper, the customer insolvency is defined to be a classification problem since our main purpose is to categorize the customer in one of the two classes: potentially insolvent or potentially solvent. Therefore, a model with precise business prediction using the knowledge discovery and Data Mining techniques on an enormous heterogeneous and noisy data is proposed. Moreover, a fuzzy approach to evaluate and analyze the customer behavior leading to segment them into groups that provide better understanding of customers is developed. These groups with many other significant variables feed into a classification algorithm to classify the customers. A real case study is considered here, followed by analysis and comparison of the results for the reason to select the best classification model that maximizes the accuracy for insolvent customers and minimizes the error rate in the misclassification of solvent customers.
Does Agile work ? — A quantitative analysis of agile project success

The Agile project management methodology has been widely used in recent years as a means to counter the dangers of traditional, front-end planning methods that often lead to downstream development pathologies. Although numerous authors have pointed to the advantages of Agile, with its emphasis on individuals and interactions over processes, customer collaboration over contracts and formal negotiations, and responsiveness over rigid planning, there are, to date, very few large-scale, empirical studies to support the contention that Agile methods can improve the likelihood of project success. Developed originally for software development, it is still predominantly an IT phenomenon. But due to its success it has now spread to non-IT projects. Using a data sample of 1002 projects across multiple industries and countries, we tested the effect of Agile use in organizations on two dimensions of project success: efficiency and overall stakeholder satisfaction against organizational goals. We further examined the moderating effects of variables such as perceived quality of the vision/goals of the project, project complexity, and project team experience. Our findings suggest that Agile methods do have a positive impact on both dimensions of project success. Further, the quality of the vision/goals is a marginally significant moderator of this effect. Implications of these findings and directions for future research are discussed.
Transfer Learning for Named-Entity Recognition with Neural Networks

Recent approaches based on artificial neural networks (ANNs) have shown promising results for named-entity recognition (NER). In order to achieve high performances, ANNs need to be trained on a large labeled dataset. However, labels might be difficult to obtain for the dataset on which the user wants to perform NER: label scarcity is particularly pronounced for patient note de-identification, which is an instance of NER. In this work, we analyze to what extent transfer learning may address this issue. In particular, we demonstrate that transferring an ANN model trained on a large labeled dataset to another dataset with a limited number of labels improves upon the state-of-the-art results on two different datasets for patient note de-identification.
Cyber Hate Classification: 'Othering' Language And Paragraph Embedding

Offensive or antagonistic language targeted at individuals and social groups based on their personal characteristics (also known as cyber hate speech or cyberhate) has been frequently posted and widely circulated via the World Wide Web. This can be considered as a key risk factor for individual and societal tension linked to regional instability. Automated Web-based cyberhate detection is important for observing and understanding community and regional societal tension especially in online social networks where posts can be rapidly and widely viewed and disseminated. While previous work has involved using lexicons, bags-of-words or probabilistic language parsing approaches, they often suffer from a similar issue which is that cyberhate can be subtle and indirect thus depending on the occurrence of individual words or phrases can lead to a significant number of false negatives, providing inaccurate representation of the trends in cyberhate. This problem motivated us to challenge thinking around the representation of subtle language use, such as references to perceived threats from ’the other’ including immigration or job prosperity in a hateful context. We propose a novel framework that utilises language use around the concept of ’othering’ and intergroup threat theory to identify these subtleties and we implement a novel classification method using embedding learning to compute semantic distances between parts of speech considered to be part of an ’othering’ narrative. To validate our approach we conduct several experiments on different types of cyberhate, namely religion, disability, race and sexual orientation, with F-measure scores for classifying hateful instances obtained through applying our model of 0.93, 0.86, 0.97 and 0.98 respectively, providing a significant improvement in classifier accuracy over the state-of-the-art.