nils-herrmann/clean_scirepeval huggingface dataset:
Learning Important Features Through Propagating Activation Differences

The purported “black box” nature of neural networks is a barrier to adoption in applications where interpretability is essential. Here we present DeepLIFT (Deep Learning Important FeaTures), a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input. DeepLIFT compares the activation of each neuron to its ‘reference activation’ and assigns contribution scores according to the difference. By optionally giving separate consideration to positive and negative contributions, DeepLIFT can also reveal dependencies which are missed by other approaches. Scores can be computed efficiently in a single backward pass. We apply DeepLIFT to models trained on MNIST and simulated genomic data, and show significant advantages over gradient-based methods.
Efficient Realization of Coordinate Structures in Combinatory Categorial Grammar

We describe a chart realization algorithm for Combinatory Categorial Grammar (CCG), and show how it can be used to efficiently realize a wide range of coordination phenomena, including argument cluster coordination and gapping. The algorithm incorporates three novel methods for improving the efficiency of chart realization: (i) using rules to chunk the input logical form into sub-problems to be solved independently prior to further combination; (ii) pruning edges from the chart based on the n-gram score of the edge’s string, in comparison to other edges with equivalent categories; and (iii) formulating the search as a best-first anytime algorithm, using n-gram scores to sort the edges on the agenda. The algorithm has been implemented as an extension to the OpenCCG open source CCG parser, and initial performance tests indicate that the realizer is fast enough for practical use in natural language dialogue systems.
Data Science , Predictive Analytics , and Big Data : A Revolution That Will Transform Supply Chain Design and Management

W e illuminate the myriad of opportunities for research where supply chain management (SCM) intersects with data science, predictive analytics, and big data, collectively referred to as DPB. We show that these terms are not only becoming popular but are also relevant to supply chain research and education. Data science requires both domain knowledge and a broad set of quantitative skills, but there is a dearth of literature on the topic and many questions. We call for research on skills that are needed by SCM data scientists and discuss how such skills and domain knowledge affect the effectiveness of an SCM data scientist. Such knowledge is crucial to develop future supply chain leaders. We propose definitions of data science and predictive analytics as applied to SCM. We examine possible applications of DPB in practice and provide examples of research questions from these applications, as well as examples of research questions employing DPB that stem from management theories. Finally, we propose specific steps interested researchers can take to respond to our call for research on the intersection of SCM and DPB.
Why an Intelligence Explosion is Probable

If a future Artificial Intelligence were to reach the level of human intelligence there is a possibility that it would be able to rapidly redesign itself until its own capabilities far exceeded those of human beings. We analyze the principle factors that might govern the rapidity of this ‘intelligence explosion’ process. We argue that if degree of intelligence is defined using a relatively uncontroversial measure that involves only the relative speed of thought with respect to that of the human mind, and if an intelligence explosion is defined as a thousandfold increase in speed, then there are compelling reasons to believe that none of the barriers to this process look plausible, and therefore an intelligence explosion is highly likely.
Unsupervised Segmentation of 3D Medical Images Based on Clustering and Deep Representation Learning

This paper presents a novel unsupervised segmentation method for 3D medical images. Convolutional neural networks (CNNs) have brought significant advances in image segmentation. However, most of the recent methods rely on supervised learning, which requires large amounts of manually annotated data. Thus, it is challenging for these methods to cope with the growing amount of medical images. This paper proposes a unified approach to unsupervised deep representation learning and clustering for segmentation. Our proposed method consists of two phases. In the first phase, we learn deep feature representations of training patches from a target image using joint unsupervised learning (JULE) that alternately clusters representations generated by a CNN and updates the CNN parameters using cluster labels as supervisory signals. We extend JULE to 3D medical images by utilizing 3D convolutions throughout the CNN architecture. In the second phase, we apply k-means to the deep representations from the trained CNN and then project cluster labels to the target image in order to obtain the fully segmented image. We evaluated our methods on three images of lung cancer specimens scanned with micro-computed tomography (micro-CT). The automatic segmentation of pathological regions in micro-CT could further contribute to the pathological examination process. Hence, we aim to automatically divide each image into the regions of invasive carcinoma, noninvasive carcinoma, and normal tissue. Our experiments show the potential abilities of unsupervised deep representation learning for medical image segmentation.
Intrinsic and Extrinsic Motivations: Classic Definitions and New Directions.

Intrinsic and extrinsic types of motivation have been widely studied, and the distinction between them has shed important light on both developmental and educational practices. In this review we revisit the classic definitions of intrinsic and extrinsic motivation in light of contemporary research and theory. Intrinsic motivation remains an important construct, reflecting the natural human propensity to learn and assimilate. However, extrinsic motivation is argued to vary considerably in its relative autonomy and thus can either reflect external control or true self-regulation. The relations of both classes of motives to basic human needs for autonomy, competence and relatedness are discussed.
Dynamic Resource Discovery Based on Preference and Movement Pattern Similarity for Large-Scale Social Internet of Things

Given the wide range deployment of disconnected delay-tolerant social Internet of Things (SIoT), efficient resource discovery remains a fundamental challenge for large-scale SIoT. The existing search mechanisms over the SIoT do not consider preference similarity and are designed in Cartesian coordinates without sufficient consideration of real-world network deployment environments. In this paper, we propose a novel resource discovery mechanism in a 3-D Cartesian coordinate system with the aim of enhancing the search efficiency over the SIoT. Our scheme is based on both of preference and movement pattern similarity to achieve higher search efficiency and to reduce the system overheads of SIoT. Simulation experiments have been conducted to evaluate this new scheme in a large-scale SIoT environment. The simulation results show that our proposed scheme outperforms the state-of-the-art resource discovery schemes in terms of search efficiency and average delay.
Enabling Public Verifiability and Data Dynamics for Storage Security

Cloud Computing has been envisioned as the next-generation architecture of IT Enterprise. It moves the application software and databases to the centralized large data centers, where the management of the data and services may not be fully trustworthy. This unique paradigm brings about many new security challenges, which have not been well understood. This work studies the problem of ensuring the integrity of data storage in Cloud Computing. In particular, we consider the task of allowing a third party auditor (TPA), on behalf of the cloud client, to verify the integrity of the dynamic data stored in the cloud. The introduction of TPA eliminates the involvement of client through the auditing of whether his data stored in the cloud is indeed intact, which can be important in achieving economies of scale for Cloud Computing. The support for data dynamics via the most general forms of data operation, such as block modification, insertion and deletion, is also a significant step toward practicality, since services in Cloud Computing are not limited to archive or backup data only. While prior works on ensuring remote data integrity often lacks the support of either public verifiability or dynamic data operations, this paper achieves both. We first identify the difficulties and potential security problems of direct extensions with fully dynamic data updates from prior works and then show how to construct an elegant verification scheme for seamless integration of these two salient features in our protocol design. In particular, to achieve efficient data dynamics, we improve the Proof of Retrievability model  by manipulating the classic Merkle Hash Tree (MHT) construction for block tag authentication. Extensive security and performance analysis show that the proposed scheme is highly efficient and provably secure.
A Deep-Local-Global Feature Fusion Framework for High Spatial Resolution Imagery Scene Classification

High spatial resolution (HSR) imagery scene classification has recently attracted increased attention. The bag-of-visual-words (BoVW) model is an effective method for scene classification. However, it can only extract handcrafted features, and it disregards the spatial layout information, whereas deep learning can automatically mine the intrinsic features as well as preserve the spatial location, but it may lose the characteristic information of the HSR images. Although previous methods based on the combination of BoVW and deep learning have achieved comparatively high classification accuracies, they have not explored the combination of handcrafted and deep features, and they just used the BoVW model as a feature coding method to encode the deep features. This means that the intrinsic characteristics of these models were not combined in the previous works. In this paper, to discover more discriminative semantics for HSR imagery, the deep-local-global feature fusion (DLGFF) framework is proposed for HSR imagery scene classification. Differing from the conventional scene classification methods, which utilize only handcrafted features or deep features, DLGFF establishes a framework integrating multi-level semantics from the global texture feature–based method, the BoVW model, and a pre-trained convolutional neural network (CNN). In DLGFF, two different approaches are proposed, i.e., the local and global features fused with the pooling-stretched convolutional features (LGCF) and the local and global features fused with the fully connected features (LGFF), to exploit the multi-level semantics for complex scenes. The experimental results obtained with three HSR image classification datasets confirm the effectiveness of the proposed DLGFF framework. Compared with the published results of the previous scene classification methods, the classification accuracies of the DLGFF framework on the 21-class UC Merced dataset and 12-class Google dataset of SIRI-WHU can reach 99.76%, which is superior to the current state-of-the-art methods. The classification accuracy of the DLGFF framework on the 45-class NWPU-RESISC45 dataset, 96.37 ± 0.05%, is an increase of about 6% when compared with the current state-of-the-art methods. This indicates that the fusion of the global low-level feature, the local mid-level feature, and the deep high-level feature can provide a representative description for HSR imagery.
Real-time facial animation on mobile devices

We present a performance-based facial animation system capable of running on mobile devices at real-time frame rates. A key component of our system is a novel regression algorithm that accurately infers the facial motion parameters from 2D video frames of an ordinary web camera. Compared with the state-of-the-art facial shape regression algorithm , which takes a two-step procedure to track facial animations (i.e., first regressing the 3D positions of facial landmarks, and then computing the head poses and expression coefficients), we directly regress the head poses and expression coefficients. This one-step approach greatly reduces the dimension of the regression target and significantly improves the performance of the tracking process while preserving the tracking accuracy. We further propose to collect the training images of the user under different lighting environments, and make use of the data to learn a user-specific regressor, which can robustly handle lighting changes that frequently occur when using mobile devices.
Automated plant Watering system

In daily operations related to farming or gardening Watering is the most important cultural practice and the most labor-intensive task. No matter whichever weather it is, either too hot and dry or too cloudy and wet, you want to be able to control the amount of water that reaches your plants. Modern watering systems could be effectively used to water plants when they need it. But this manual process of watering requires two important aspects to be considered: when and how much to water. In order to replace manual activities and making gardener's work easier, we have create automatic plant watering system. By adding automated plant watering system to your garden or agricultural field, you will help all of your plants reach their fullest potential as well as conserving water. Using sprinklers drip emitters, or a combination of both, we can design a system that is ideal for every plant in our yard. For implementation of automatic plant watering system, we have used combination of sprinkler systems, pipes, and nozzles. This project uses the ATmega328 microcontroller. It is programmed to sense moisture level of plants at particular instance of time, if the moisture content is less than specified threshold which is predefined according to particular plant's water need then desired amount of water is supplied till it reaches threshold. Generally, plants need to be watered twice a day, morning and evening. Thus, the microcontroller is programmed to water plants two times per day. System is designed in such a way that it reports its current state as well as remind the user to add water to the tank. All this notifications are made through mobile application. We hope that through this prototype we all can enjoy having plants, without being worried about absent or forgetfulness.
Learning to Listen to Your Ego-(motion): Metric Motion Estimation from Auditory Signals

This paper is about robot ego-motion estimation relying solely on acoustic sensing. By equipping a robot with microphones, we investigate the possibility of employing the noise generated by the motors and actuators of the vehicle to estimate its motion. Audio-based odometry is not affected by the scene’s appearance, lighting conditions, and structure. This makes sound a compelling auxiliary source of information for ego-motion modelling in environments where more traditional methods, such as those based on visual or laser odometry, are particularly challenged. By leveraging multi-task learning and deep architectures, we provide a regression framework able to estimate the linear and the angular velocity at which the robot has been travelling. Our experimental evaluation conducted on approximately two hours of data collected with an unmanned outdoor field robot demonstrated an absolute error lower than 0.07 m/s and 0.02 rad/s for the linear and angular velocity, respectively. When compared to a baseline approach, making use of single-task learning scheme, our system shows an improvement of up to 26% in the ego-motion estimation.
Reinforcement-Learning-Based Intelligent Maximum Power Point Tracking Control for Wind Energy Conversion Systems

This paper proposes an intelligent maximum power point tracking (MPPT) algorithm for variable-speed wind energy conversion systems (WECSs) based on the reinforcement learning (RL) method. The model-free Q-learning algorithm is used by the controller of the WECS to learn a map from states to optimal control actions online by updating the action values according to the received rewards. The experienced action values are stored in a Q-table, based on which the maximum power points (MPPs) are obtained after a certain period of online learning. The learned MPPs are then used to generate an optimum speed-power curve for fast MPPT control of the WECS. Since RL enables the WECS to learn by directly interacting with the environment, knowledge of wind turbine parameters or wind speed information is not required. The proposed MPPT control algorithm is validated by simulation studies for a 1.5-MW doubly-fed induction generator-based WECS and experimental results for a 200-W permanent-magnet synchronous generator-based WECS emulator.
Family History of Migraine Associated With Posttraumatic Migraine Symptoms Following Sport-Related Concussion.

To determine whether family history of migraine increased the likelihood of posttraumatic migraine (PTM) symptom presentation in adolescents following concussion, and examine the influence of family history of migraine and PTM on postinjury outcomes.



Outpatient concussion clinic.



A total of 153 patients with concussion (103 males and 50 females) aged 15.72 ± 1.48 years (range 12-18 years).



Cross-sectional, observational study of patients presenting for initial evaluation 4.72 ± 3.05 days (range 1-14) postinjury.



Computerized neurocognitive testing, symptom report, and vestibular/oculomotor screening.



Patients with a family history of migraine were 2.6 times (odds ratio = 2.60, confidence interval = 1.35-5.02, P = .003) more likely to present with PTM compared with patients without a family history. Results of multivariate analyses of covariance, controlling for concussion history, revealed significant main effects for PTM on (1) Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT)/Post-Concussion Symptom Scale (PCSS) (F = 15.43, P <.001) and (2) Vestibular/Ocular Motor Screening (VOMS) (F = 8.52, P < .001). There was no main effect for family history of migraine on ImPACT/PCSS (P = .22) and VOMS (P = .83) or interaction between family history of migraine and PTM on ImPACT/PCSS (P = .84) and VOMS (P = .52).



Family history of migraine is associated with PTM symptoms following sport-related concussion, suggesting a genetic predisposition for migraine may serve as a catalyst or trigger for onset of PTM. However, only presence of PTM, rather than family history of migraine, was related to worse neurocognitive and vestibular/oculomotor outcomes.
Graphene-Fiber-Based Supercapacitors Favor N-Methyl-2-pyrrolidone/Ethyl Acetate as the Spinning Solvent/Coagulant Combination.

One-dimensional flexible fiber supercapacitors (FSCs) have attracted great interest as promising energy-storage units that can be seamlessly incorporated into textiles via weaving, knitting, or braiding. The major challenges in this field are to develop tougher and more efficient FSCs with a relatively easy and scalable process. Here, we demonstrate a wet-spinning process to produce graphene oxide (GO) fibers from GO dispersions in N-methyl-2-pyrrolidone (NMP), with ethyl acetate as the coagulant. Upon chemical reduction of GO, the resulting NMP-based reduced GO (rGO) fibers (rGO@NMP-Fs) are twice as high in the surface area and toughness but comparable in tensile strength and conductivity as that of the water-based rGO fibers (rGO@H2O-Fs). When assembled into parallel FSCs, rGO@NMP-F-based supercapacitors (rGO@NMP-FSCs) offered a specific capacitance of 196.7 F cm-3 (147.5 mF cm-2), five times higher than that of rGO@H2O-F-based supercapacitors (rGO@H2O-FSCs) and also higher than most existing wet-spun rGO-FSCs, as well as those FSCs built with metal wires, graphene/carbon nanotube (CNT) fibers, or even pseudocapacitive materials. In addition, our rGO@NMP-FSCs can provide good bending and cycling stability. The energy density of our rGO@NMP-FSCs reaches ca. 6.8 mWh cm-3, comparable to that of a Li thin-film battery (4 V/500 μAh).
Point cloud labeling using 3D Convolutional Neural Network

In this paper, we tackle the labeling problem for 3D point clouds. We introduce a 3D point cloud labeling scheme based on 3D Convolutional Neural Network. Our approach minimizes the prior knowledge of the labeling problem and does not require a segmentation step or hand-crafted features as most previous approaches did. Particularly, we present solutions for large data handling during the training and testing process. Experiments performed on the urban point cloud dataset containing 7 categories of objects show the robustness of our approach.
Power Load Forecasting with Least Squares Support Vector Machines and Chaos Theory

In this paper, a novel approach to power load forecasting based on least squares support vector machines (LS-SVM) and chaos theory is presented. First, with the data from EUNITE network, we find the chaotic characteristics of the daily peak load series by analyzing the largest Lyapunov exponent and power spectrum. Average mutual information (AMI) method is used to find the optimal time lag. Then the time series is decomposed by wavelet transform. Cao's method is adopted to find the optimal embedding dimension of the decomposed series of each level. At last, with the optimal time lag and embedding dimension, LS-SVM is used to predict future load series of each level. The reconstruction of predicted time series is used as the final forecasting result. The mean absolute percentage error (MAPE) is 1.1013% and the maximum error is 25.1378 MW, which show that this approach is applicable for power load forecasting
Accelerating convolutional neural networks by group-wise 2D-filter pruning

Network pruning is an effective way to accelerate Convolutional Neural Networks (CNNs). In recent years, structured pruning methods are proposed in favor of unstructured methods as they have shown greater speedup in practical use. Existing structured methods does pruning along two main dimensions: 3D-filter wise, i.e., remove a 3D-fllter as a whole, and filter-shape wise, i.e., remove a same position from all 3D-filters. In this work, we propose a new group-wise 2D-fllter pruning approach that is orthogonal and complementary to the existing methods. The proposed approach removes a portion of 2D-fllters from each 3D-filter according to the pruning patterns learned from the data, and leads to compressed models that do not require sophisticated implementation of convolution operations. A fine-tuning process is followed to recover the accuracy. The knowledge distillation (KD) framework is explored in the fine-tuning process to improve the performance. We present our method for learning the pruning pattens as well as the fine-tuning strategy based on knowledge distillation. The proposed approach is validated on two representative CNN models — ZF and VGG16, pre-trained on ILSVRC12. Experimental results demonstrate the effectiveness of our approach. In VGG16, we get even higher accuracy after speeding-up the network by 4 times.