nils-herrmann/clean_scirepeval huggingface dataset:
Why Nouns Are Learned before Verbs: Linguistic Relativity Versus Natural Partitioning. Technical Report No. 257.

There is overwhelming evidence that children's first words are primarily nouns even across languages. These data are interpreted as evidence of a "Natural Partitions Theory," one that holds that the concepts referred to by nouns are conceptually more basic than those referred to by verbs or prepositions. Analysis of data from cross-linguistic studies designed to eliminate Inonconceptual differences between words in a language that might account for the Larlier acquisition of nouns--such as the position of a word in the sentence, morphological transparency (the ease with which the root can be heard in various uses of the word), and patterns of language teaching strategies--leaves some version of the Natural Partitions hypothesis as the most reasonable view of early vocabulary acquisition. A cross-linguistic examination of a single sentence suggests that if objecthood is created by spatial relations among perceptual elements, then good concrete objects are particularly cohesive collections of percepts. These highly cohesive collections of perceptual information tend to be lexicalized as nouns by almost every language. Object concepts are given to language users by the world and can be learned one at a time, while predicate concepts form a system in each language., that, from a child's point of view, must be discovered.
Path Integral Networks: End-to-End Differentiable Optimal Control

In this paper, we introduce Path Integral Networks (PI-Net), a recurrent network representation of the Path Integral optimal control algorithm. The network includes both system dynamics and cost models, used for optimal control based planning. PI-Net is fully differentiable, learning both dynamics and cost models end-to-end by back-propagation and stochastic gradient descent. Because of this, PI-Net can learn to plan. PI-Net has several advantages: it can generalize to unseen states thanks to planning, it can be applied to continuous control tasks, and it allows for a wide variety learning schemes, including imitation and reinforcement learning. Preliminary experiment results show that PI-Net, trained by imitation learning, can mimic control demonstrations for two simulated problems; a linear system and a pendulum swing-up problem. We also show that PI-Net is able to learn dynamics and cost models latent in the demonstrations.
A Conceptual Analysis of Capabilities: Identifying and Classifying Sources of Competitive Advantage in the Wood Industry

The resource-based, dynamic-capability and knowledge-based views of the firm focus on identifying company-internal success factors. However, their empirical research and its managerial implications suffer from a lack of conceptual clarity and understanding of the structural and hierarchical complexity of resources and capabilities. This paper suggests a three-step capability-classification system that takes into account 1) the division between company-specific and industry-significant capabilities, 2) hierarchies of task-specific, functional and cross-functional capabilities, and 3) the structure of individual capabilities. We tested the usability of the theoretical construct by identifying and classifying the capabilities of the leading North American and European wood-industry case companies. Our study introduces conceptual tools for those aiming to do empirical research on capabilities, explores the interaction between a firm’s capabilities and its competitive environment, and exposes the capability portfolio of wood-industry companies.
Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection

In this study, a hierarchical electroencephalogram (EEG) classification system for epileptic seizure detection is proposed. The system includes the following three stages: (i) original EEG signals representation by wavelet packet coefficients and feature extraction using the best basis-based wavelet packet entropy method, (ii) cross-validation (CV) method together with k-Nearest Neighbor (k-NN) classifier used in the training stage to hierarchical knowledge base (HKB) construction, and (iii) in the testing stage, computing classification accuracy and rejection rate using the top-ranked discriminative rules from the HKB. The data set is taken from a publicly available EEG database which aims to differentiate healthy subjects and subjects suffering from epilepsy diseases. Experimental results show the efficiency of our proposed system. The best classification accuracy is about 100% via 2-, 5-, and 10-fold cross-validation, which indicates the proposed method has potential in designing a new intelligent EEG-based assistance diagnosis system for early detection of the electroencephalographic changes.
Brain mechanisms of pain affect and pain modulation

Recent animal studies reveal ascending nociceptive and descending modulatory pathways that may contribute to the affective-motivational aspects of pain and play a critical role in the modulation of pain. In humans, a reliable pattern of cerebral activity occurs during the subjective experience of pain. Activity within the anterior cingulate cortex and possibly in other classical limbic structures, appears to be closely related to the subjective experience of pain unpleasantness and may reflect the regulation of endogenous mechanisms of pain modulation.
Innovative Cost Effective Approach for Cell Phone Based Remote Controlled Embedded System for Irrigation

This work describes the development of innovative low cost cell phone based remote control application for induction motor-pump based irrigation in agriculture. Rural areas in many states of India are plagued by frequent power cuts and abnormal voltage conditions. The developed system ensures that water is distributed to field whenever normal conditions exist based on task specified. A novel concept of miscall for specified duration has been used to reduce the operational cost of the system and for the convenience of farmers facing difficulty in typing messages. Information is exchanged in form of miscalls / message between the system and the user cell phones. The system is based on AVR ATMega32 micro-controller and includes protection against single phasing, over-current, dry running and other desirable features. DS1307 and DS18S20 are used for time and temperature measurement respectively. It is expected that system will relieve hardships of farmers relating water distribution to a great extent.
Introducing teachers to computational thinking using unplugged storytelling

Many countries are introducing new school computing syllabuses that make programming and computational thinking core components. However, many of the teachers involved have major knowledge, skill and pedagogy gaps. We have explored the effectiveness of using 'unplugged' methods (constructivist, often kinaesthetic, activities away from computers) with contextually rich storytelling to introduce teachers to these topics in a non-threatening way. We describe the approach we have used in workshops for teachers and its survey based evaluation. Teachers were highly positive that the approach was inspiring, confidence building and gave them a greater understanding of the concepts involved, as well as giving practical teaching techniques that they would use.
Using Lexical Resources for Irony and Sarcasm Classification

The paper presents a language dependent model for classification of statements into ironic and non-ironic. The model uses various language resources: morphological dictionaries, sentiment lexicon, lexicon of markers and a WordNet based ontology. This approach uses various features: antonymous pairs obtained using the reasoning rules over the Serbian WordNet ontology (R), antonymous pairs in which one member has positive sentiment polarity (PPR), polarity of positive sentiment words (PSP), ordered sequence of sentiment tags (OSA), Part-of-Speech tags of words (POS) and irony markers (M). The evaluation was performed on two collections of tweets that had been manually annotated according to irony. These collections of tweets as well as the used language resources are in the Serbian language (or one of closely related languages --Bosnian/Croatian/Montenegrin). The best accuracy of the developed classifier was achieved for irony with a set of 5 features -- (PPR, PSP, POS, OSA, M) -- acc = 86.1%, while for sarcasm the best results were achieved with the set (R, PSP, POS, OSA, M) -- acc = 72.8.
Examining the relationship between game type, player disposition and aggression

As technology advances the need for more comprehensive analyses of the relationship between human aggression and gaming increases. First Person Shooter gaming environments provide realistic opportunities for many interactions between players. Violent and aggressive video games have been blamed for many aggressive acts. Early meta-analyses of gaming and aggression concluded that gaming does increase aggression, although at levels lower than that of television and other media. More recent meta-analyses claim that there is no correlation between gaming and aggression. However, previous studies do not consider the possible effect of a live opponent on player aggression and therefore the current study includes this as a putative factor in gaming and aggression. Results indicate that neither the disposition of the opponent (confederate) nor the aggressive level of the game type affected participant aggression.
Glioblastoma multiforme tissue histopathology images based disease stage classification with deep CNN

Recently, many feature extraction methods for histopathology images have been reported for automatic quantitative analysis. One of the severe brain tumors is the Glioblastoma multiforme (GBM) and histopathology tissue images can provide unique insights into identifying and grading disease stages. However, the number of tissue samples to be examined is enormous, and is a burden to pathologists because of tedious manual evaluation traditionally required for efficient evaluation. In this study, we consider feature extraction and disease stage classification for brain tumor histopathology images using automatic image analysis methods. In particular, we utilized an automatic feature extraction and labeling for histopathology imagery data given by The Cancer Genome Atlas (TCGA) and checked the classification accuracy of disease stages in GBM tissue images using deep Convolutional Neural Network (CNN). Experimental results indicate promise in automatic disease stage classification and high level of accuracy were obtained for tested image data.
Assessing Effects of Task and Data Distribution on the Effectiveness of Visual Encodings

In addition to the choice of visual encodings, the effectiveness of a data visualization may vary with the analytical task being performed and the distribution of data values. To better assess these effects and create refined rankings of visual encodings, we conduct an experiment measuring subject performance across task types (e.g., comparing individual versus aggregate values) and data distributions (e.g., with varied cardinalities and entropies). We compare performance across 12 encoding specifications of trivariate data involving 1 categorical and 2 quantitative fields, including the use of x, y, color, size, and spatial subdivision (i.e., faceting). Our results extend existing models of encoding effectiveness and suggest improved approaches for automated design. For example, we find that colored scatterplots (with positionally-coded quantities and color-coded categories) perform well for comparing individual points, but perform poorly for summary tasks as the number of categories increases.
Device and operation mechanism for non-beacon IEEE802.15.4/Zigbee nodes running on harvested energy

The power supply is one of the main constraints when operating Wireless Sensor Networks (WSN). The most obvious energy source for a WSN node is a rechargeable or non-rechargeable battery. Batteries seriously limit WSNs usage and are associated to the increasing cost of large networks. Low cost networks will be achieved when battery consumption becomes substantially reduced or eliminated. Within this scope, powering WSN devices using mains power, is not an advantage since node installation requires complex and time consuming actions. Replacing batteries is therefore an important topic even in environments with mains power availability. In this paper, a battery-less device running an IEEE802.15.4/Zigbee protocol stack on harvested energy is proposed. Its operation mechanism and required software adaptation to sustain this complex protocol are described. The protocol compliant node uses a contact-less scavenger system that is able to power a WSN node from mains power lines thus providing an inexpensive and easily installable device even for nonskilled users.
3 bit balanced digital phase shifter using switch mode topology

3 bit balanced digital phase shifter is designed for 2.6 GHz to 2.9 GHz band. This circuit is based on switched network topology which is a active design and has number of network configuration. In the proposed designed high pass and low pass topology has been used. It gives both positive and negative phases according to control input applied. Low pass circuit is also known as reference circuit which gives positive phase while high pass is delay circuit which gives negative phase. Circuit is made balanced with wideband Marchand balun. It is designed for 2 GHz to 3 GHz band. Balanced circuit will reduce electromagnetic interference thus providing better phase shift. As phase shifters are not usually being used in commercial products due to its cost but use of lumped components will lessen its cost. Hence it is better alternative to other phase shifter circuit. Insertion loss and return loss is also good.
A Discrete Model for Inelastic Deformation of Thin Shells

We introduce a method for simulating the inelastic deformation of thin shells: we model plasticity and fracture of curved, deformable objects such as light bulbs, egg-shells and bowls. Our novel approach uses triangle meshes yet evolves fracture lines unrestricted to mesh edges. We present a novel measure of bending strain expressed in terms of surface invariants such as lengths and angles. We also demonstrate simple techniques to improve the robustness of standard timestepping as well as collisionresponse algorithms.
An Anatomical Analysis of the Supratrochlear Artery: Considerations in Facial Filler Injections and Preventing Vision Loss.

Embolia cutis medicamentosa (ECM) is a rare phenomenon attributed to intra-arterial drug injection. Glabellar filler injections can result in potentially devastating visual loss from inadvertent retrograde arteriolar embolization due to the extensive vasculature within the upper face. The minimum amount of filler necessary to potentiate this complication has not yet been reported.



We aim to determine the volume of filler necessary to occupy the supratrochlear artery from the glabella to the bifurcation of the ophthalmic and central retinal arteries. We specifically examine the volume of the supratrochlear artery from the glabella to orbital apex.



The study was approved by Duke University Institutional Review Board and involved surgical dissection of six fresh tissue cadaver heads (12 hemifaces). The arterial system in each cadaver head was injected with latex for visualization. The supratrochlear arteries were isolated anteriorly from the glabella to the orbital apex posteriorly. Intra-orbital vessel radius and length were measured. The vessel volume was calculated by water displacement of the intra-arterial latex.



The vessel volumes ranged from 0.04 to 0.12 mL. The average vessel volume was calculated as 0.085 mL, the average length as 51.75 mm, and the average radius as 0.72 mm.



Vascular occlusion from filler injections can lead to devastating visual consequences due to inadvertent retrograde intra-arterial embolization. Our findings indicate that the average entire volume of the supratrochlear artery from the glabella to the orbital apex is 0.085 mL. Injectors should be aware that a bolus of this critical volume may lead to a significant adverse outcome.
Forensics acquisition - Analysis and circumvention of samsung secure boot enforced common criteria mode

The acquisition of data from mobile phones have been a mainstay of criminal digital forensics for a number of years now. However, this forensic acquisition is getting more and more difficult with the increasing security level and complexity of mobile phones (and other embedded devices). In addition, it is often difficult or impossible to get access to design specifications, documentation and source code. As a result, the forensic acquisition methods are also increasing in complexity, requiring an ever deeper understanding of the underlying technology and its security mechanisms. Forensic acquisition techniques are turning to more offensive solutions to bypass security mechanisms, through security
Visual Abstraction and Exploration of Multi-class Scatterplots

Scatterplots are widely used to visualize scatter dataset for exploring outliers, clusters, local trends, and correlations. Depicting multi-class scattered points within a single scatterplot view, however, may suffer from heavy overdraw, making it inefficient for data analysis. This paper presents a new visual abstraction scheme that employs a hierarchical multi-class sampling technique to show a feature-preserving simplification. To enhance the density contrast, the colors of multiple classes are optimized by taking the multi-class point distributions into account. We design a visual exploration system that supports visual inspection and quantitative analysis from different perspectives. We have applied our system to several challenging datasets, and the results demonstrate the efficiency of our approach.
Computational Intelligence in Sports: A Systematic Literature Review

Recently, data mining studies are being successfully conducted to estimate several parameters in a variety of domains. Data mining techniques have attracted the attention of the information industry and society as a whole, due to a large amount of data and the imminent need to turn it into useful knowledge. However, the effective use of data in some areas is still under development, as is the case in sports, which in recent years, has presented a slight growth; consequently, many sports organizations have begun to see that there is a wealth of unexplored knowledge in the data extracted by them. Therefore, this article presents a systematic review of sports data mining. Regarding years 2010 to 2018, 31 types of research were found in this topic. Based on these studies, we present the current panorama, themes, the database used, proposals, algorithms, and research opportunities. Our findings provide a better understanding of the sports data mining potentials, besides motivating the scientific community to explore this timely and interesting topic.
Extended Kalman filter based grid synchronization in the presence of voltage unbalance for smart grid

In this paper, grid synchronization for gird-connected power generation systems in the presence of voltage unbalance and frequency variation is considered. A new extended Kalman filter based synchronization algorithm is proposed to track the phase angle of the utility network. Instead of processing the three-phase voltage signal in the abc natural reference frame and resorting to the symmetrical component transformation as in the traditional way, the proposed algorithm separates the positive and negative sequences in the transformed αβ stationary reference frame. Based on the obtained expressions in αβ domain, an EKF is developed to track both the in-phase and quadrature sinusoidal signals together with the unknown frequency. An estimate of the phase angle of the positive sequence is then obtained. As a by-product, estimates of phase angle of negative sequence and grid frequency are also computed. Compared to the commonly used scheme, the proposed algorithm has a simplified structure. The good performance is supported by computer simulations.
Human Action Recognition Using Multi-Velocity STIPs and Motion Energy Orientation Histogram

Local image features in space-time or spatio-temporal interest points provide compact and abstract representations of patterns in a video sequence. In this paper, we present a novel human action recognition method based on multi-velocity spatio-temporal interest points (MVSTIPs) and a novel local descriptor called motion energy (ME) orientation histogram (MEOH). The MVSTIP detection includes three steps: first, filtering video frames with multi-direction ME filters at different speeds to detect significant changes at the pixel level; thereafter, a surround suppression model is employed to rectify the ME deviation caused by the camera motion and complicated backgrounds (e.g., dynamic texture); finally, MVSTIPs are obtained with local maximum filters at multispeeds. After detection, we develop MEOH descriptor to capture the motion features in local regions around interest points. The performance of the proposed method is evaluated on KTH, Weizmann, and UCF sports human action datasets. Results show that our method is robust to both simple and complex backgrounds and the method is superior to other methods that are based on local features.
Optimization of Cooperative Sensing in Cognitive Radio Networks: A Sensing-Throughput Tradeoff View

In cognitive radio networks, the performance of the spectrum sensing depends on the sensing time and the fusion scheme that are used when cooperative sensing is applied. In this paper, we consider the case where the secondary users cooperatively sense a channel using the k -out-of-N fusion rule to determine the presence of the primary user. A sensing-throughput tradeoff problem under a cooperative sensing scenario is formulated to find a pair of sensing time and k value that maximize the secondary users' throughput subject to sufficient protection that is provided to the primary user. An iterative algorithm is proposed to obtain the optimal values for these two parameters. Computer simulations show that significant improvement in the throughput of the secondary users is achieved when the parameters for the fusion scheme and the sensing time are jointly optimized.
Parallel tree-ensemble algorithms for GPUs using CUDA

We present two new parallel implementations of the tree-ensemble algorithms Random Forest (RF) and Extremely randomized trees (ERT) for emerging many-core platforms, e.g., contemporary graphics cards suitable for general-purpose computing (GPGPU). Random Forest and Extremely randomized trees are ensemble learners for classification and regression. They operate by constructing a multitude of decision trees at training time and outputting a prediction by comparing the outputs of the individual trees. Thanks to the inherent parallelism of the task, an obvious platform for its computation is to employ contemporary GPUs with a large number of processing cores. Previous parallel algorithms for Random Forests in the literature are either designed for traditional multi-core CPU platforms or early history GPUs with simpler hardware architecture and relatively few number of cores. The new parallel algorithms are designed for contemporary GPUs with a large number of cores and take into account aspects of the newer hardware architectures as memory hierarchy and thread scheduling. They are implemented using the C/C++ language and the CUDA interface for best possible performance on NVidia-based GPUs. An experimental study comparing with the most important previous solutions for CPU and GPU platforms shows significant improvement for the new implementations, often with several magnitudes.