nils-herrmann/clean_scirepeval huggingface dataset:
Improved Relation Extraction with Feature-Rich Compositional Embedding Models

Compositional embedding models build a representation (or embedding) for a linguistic structure based on its component word embeddings. We propose a Feature-rich Compositional Embedding Model (FCM) for relation extraction that is expressive, generalizes to new domains, and is easy-to-implement. The key idea is to combine both (unlexicalized) handcrafted features with learned word embeddings. The model is able to directly tackle the difficulties met by traditional compositional embeddings models, such as handling arbitrary types of sentence annotations and utilizing global information for composition. We test the proposed model on two relation extraction tasks, and demonstrate that our model outperforms both previous compositional models and traditional feature rich models on the ACE 2005 relation extraction task, and the SemEval 2010 relation classification task. The combination of our model and a loglinear classifier with hand-crafted features gives state-of-the-art results. We made our implementation available for general use1.
Bipartite graph reinforcement model for web image annotation

Automatic image annotation is an effective way for managing and retrieving abundant images on the internet. In this paper, a bipartite graph reinforcement model (BGRM) is proposed for web image annotation. Given a web image, a set of candidate annotations is extracted from its surrounding text and other textual information in the hosting web page. As this set is often incomplete, it is extended to include more potentially relevant annotations by searching and mining a large-scale image database. All candidates are modeled as a bipartite graph. Then a reinforcement algorithm is performed on the bipartite graph to re-rank the candidates. Only those with the highest ranking scores are reserved as the final annotations. Experimental results on real web images demonstrate the effectiveness of the proposed model.
Socially trusted collaborative edge computing in ultra dense networks

Small cell base stations (SBSs) endowed with cloud-like computing capabilities are considered as a key enabler of edge computing (EC), which provides ultra-low latency and location-awareness for a variety of emerging mobile applications and the Internet of Things. However, due to the limited computation resources of an individual SBS, providing computation services of high quality to its users faces significant challenges when it is overloaded with an excessive amount of computation workload. In this paper, we propose collaborative edge computing among SBSs by forming SBS coalitions to share computation resources with each other, thereby accommodating more computation workload in the edge system and reducing reliance on the remote cloud. A novel SBS coalition formation algorithm is developed based on the coalitional game theory to cope with various new challenges in small-cell-based edge systems, including the co-provisioning of radio access and computing services, cooperation incentives, and potential security risks. To address these challenges, the proposed method (1) allows collaboration at both the user-SBS association stage and the SBS peer offloading stage by exploiting the ultra dense deployment of SBSs, (2) develops a payment-based incentive mechanism that implements proportionally fair utility division to form stable SBS coalitions, and (3) builds a social trust network for managing security risks among SBSs due to collaboration. Systematic simulations in practical scenarios are carried out to evaluate the efficacy and performance of the proposed method, which shows that tremendous edge computing performance improvement can be achieved.
Orthogonal nonnegative matrix tri-factorization for co-clustering: Multiplicative updates on Stiefel manifolds

Matrix factorization-based methods become popular in dyadic data analysis, where a fundamental problem, for example, is to perform document clustering or co-clustering words and documents given a term-document matrix. Nonnegative matrix tri-factorization (NMTF) emerges as a promising tool for co-clustering, seeking a 3-factor decomposition X USV with all factor matrices restricted to be nonnegative, i.e., U P 0; S P 0;V P 0: In this paper we develop multiplicative updates for orthogonal NMTF where X USV is pursued with orthogonality constraints, UU 1⁄4 I; and VV 1⁄4 I, exploiting true gradients on Stiefel manifolds. Experiments on various document data sets demonstrate that our method works well for document clustering and is useful in revealing polysemous words via co-clustering words and documents.
Particle-based fluid-fluid interaction

The interesting and complex behavior of fluids emerges mainly from interaction processes. While interactions of fluids with static or dynamic solids has caught some attention in computer graphics lately, the mutual interaction of different types of fluids such as air and water or water and wax has received much less attention although these types of interaction are the basis for a variety of important phenomena.In this paper we propose a new technique to model fluid-fluid interaction based on the Smoothed Particle Hydrodynamics (SPH) method. For the simulation of air-water interaction, air particles are generated on the fly only where needed. We also model dynamic phase changes and interface forces. Our technique makes possible the simulation of phenomena such as boiling water, trapped air and the dynamics of a lava lamp.
Distance Estimation Algorithm for Stereo Pair Images

Various algorithms have been proposed for estimating camera distance from objects in digital stereo pair videos and images. Existing algorithms assume that digital images were taken with pan and tilt cameras or assume that the angles between the cameras and the object are known. The algorithm that we are proposing has the ability to calculate distance from the camera plane to the object plane, where the cameras do not have the ability to pan and tilt nor the angles between the cameras and the object are known beforehand.
Implementation of Fuzzy and PID Controller to Water Level System using LabView

The paper is about the water level control system which is required in many industrial processes like water treatment plant, the idea of the process is to keep the tank water level at the desired set point. First we implemented the conventional proportional – integral derivative (PID) controller to eliminate the steady state error, then it was used Fuzzy logic controllers which gives more performance and stability. It is noted from the results that the Fuzzy Logic controller is more enhanced than PID controller in which its no overshoot, faster settling time, better set point tracking and produced lower performances like integral of time and absolute error (ITAE) integral of time and squared error (ITSE),and integral absolute error (IAE), integral squared error (ISE). Both PID and fuzzy controller are implemented using LabView software
Selective Search for Object Recognition

This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the strength of both an exhaustive search and segmentation. Like segmentation, we use the image structure to guide our sampling process. Like exhaustive search, we aim to capture all possible object locations. Instead of a single technique to generate possible object locations, we diversify our search and use a variety of complementary image partitionings to deal with as many image conditions as possible. Our selective search results in a small set of data-driven, class-independent, high quality locations, yielding 99 % recall and a Mean Average Best Overlap of 0.879 at 10,097 locations. The reduced number of locations compared to an exhaustive search enables the use of stronger machine learning techniques and stronger appearance models for object recognition. In this paper we show that our selective search enables the use of the powerful Bag-of-Words model for recognition. The selective search software is made publicly available (Software: http://disi.unitn.it/~uijlings/SelectiveSearch.html ).
"You've Got E-Mail!" ... Shall I Deal With It Now? Electronic Mail From the Recipient's Perspective

This article considers the nature of e-mail from the recipient’s perspective—what the seeminglyfreeandeasycommunicationreallycosts therecipient. Informationgathered by electronic monitoring software is shown to be at odds with the results of an online survey of e-mail users’ perceptions of their e-mail experience—users drastically underestimate the disruptive effects of e-mail. The conclusion is that the constant monitoring of e-mail actually reduces productivity and that there is a need for increased power, control,andawarenessonthepartof thee-mail recipient toensure thate-mail remainsatool rather than a tyrant. It is necesssary to alert the user of the true cost of e-mail alerts.
Fuzzy Cognitive Map software

Fuzzy Cognitive Map (FCM) is a soft computing modelling methodology for complex systems. Beyond the mathematical formulation of the FCM theory, there was a need of developing a software tool to facilitate the implementation of FCMs. This paper describes the use of a software tool that was developed to construct FCM models. Some theoretical elements of Fuzzy Cognitive Maps are presented. Then, it is discussed how the software tool is used to develop a model and simulate the use of Fuzzy Cognitive Maps. The user interface of the software tool is described and how the FCM-Analyst is used to facilitate the implementation and simulation of Fuzzy Cognitive Maps.
Exploiting novelty, coverage and balance for topic-focused multi-document summarization

Novelty, coverage and balance are important requirements in topic-focused summarization, which to a large extent determine the quality of a summary. In this paper, we propose a novel method that incorporates these requirements into a sentence ranking probability model. It differs from the existing methods in that the novelty, coverage and balance requirements are all modeled w.r.t. a given topic, so that summaries are highly relevant to the topic and at the same time comply with topic-aware novelty, coverage and balance. Experimental results on the DUC 2005, 2006 and 2007 benchmark data sets demonstrate the effectiveness of our method.
Theoretical analysis of trend vanishing moments for directional orthogonal transforms

This work contributes to investigate theoretical properties of the trend vanishing moment (TVM) which the authors have defined in a previous work and applied to the directional design of 2-D nonsep-arable GenLOTs. The TVM condition can be regarded as a natural extention of 1-D VM to 2-D one and alternative of the conventional directional vanishing moment (DVM). Firstly, the definition of TVM is given to clarify what it is, and then the mathematical meaning is discussed. While the conventional DVM condition requires for the moments to vanish along lines in the frequency domain and restricts the direction to a rational factor, the TVM condition imposes the moments only point-wisely and the direction can be steered flexibly. Some significant properties of TVMs are also shown and a simulation result of zonal coding for an artifical picture is given to verify the trend surface annihilation property.
Accurate age classification of 6 and 12 month-old infants based on resting-state functional connectivity magnetic resonance imaging data

Human large-scale functional brain networks are hypothesized to undergo significant changes over development. Little is known about these functional architectural changes, particularly during the second half of the first year of life. We used multivariate pattern classification of resting-state functional connectivity magnetic resonance imaging (fcMRI) data obtained in an on-going, multi-site, longitudinal study of brain and behavioral development to explore whether fcMRI data contained information sufficient to classify infant age. Analyses carefully account for the effects of fcMRI motion artifact. Support vector machines (SVMs) classified 6 versus 12 month-old infants (128 datasets) above chance based on fcMRI data alone. Results demonstrate significant changes in measures of brain functional organization that coincide with a special period of dramatic change in infant motor, cognitive, and social development. Explorations of the most different correlations used for SVM lead to two different interpretations about functional connections that support 6 versus 12-month age categorization.
Analyzing the language of food on social media

We investigate the predictive power behind the language of food on social media. We collect a corpus of over three million food-related posts from Twitter and demonstrate that many latent population characteristics can be directly predicted from this data: overweight rate, diabetes rate, political leaning, and home geographical location of authors. For all tasks, our language-based models significantly outperform the majority-class baselines. Performance is further improved with more complex natural language processing, such as topic modeling. We analyze which textual features have greatest predictive power for these datasets, providing insight into the connections between the language of food, geographic locale, and community characteristics. Lastly, we design and implement an online system for real-time query and visualization of the dataset. Visualization tools, such as geo-referenced heatmaps and temporal histograms, allow us to discover more complex, global patterns mirrored in the language of food.
Practical Issues in Implementing Analog-to-Information Converters

The stability and programmability of digital signal processing systems has motivated engineers to move the analog-to-digital conversion (ADC) process closer and closer to the front end of many signal processing systems in order to perform as much processing as possible in the digital domain. Unfortunately, many important applications, including radar and communication systems, involve wideband signals that seriously stress modern ADCs; sampling these signals above the Nyquist rate is in some cases challenging and in others impossible. While wideband signals by definition have a large bandwidth, often the amount of information they carry per second is much lower; that is, they are compressible in some sense. The first contribution of this paper is a new framework for wideband signal acquisition purpose-built for compressible signals that enables sub-Nyquist data acquisition via an analog-to-information converter (AIC). The framework is based on the recently developed theory of compressive sensing in which a small number of non-adaptive, randomized measurements are sufficient to reconstruct compressible signals. The second contribution of this paper is an AIC implementation design and study of the tradeoffs and non-idealities introduced by real hardware. The goal is to identify and optimize the parameters that dominate the overall system performance
3D-Printing Spatially Varying BRDFs

A new method fabricates custom surface reflectance and spatially varying bidirectional reflectance distribution functions (svBRDFs). Researchers optimize a microgeometry for a range of normal distribution functions and simulate the resulting surface's effective reflectance. Using the simulation's results, they reproduce an input svBRDF's appearance by distributing the microgeometry on the printed material's surface. This method lets people print svBRDFs on planar samples with current 3D printing technology, even with a limited set of printing materials. It extends naturally to printing svBRDFs on arbitrary shapes.
The power of both choices: Practical load balancing for distributed stream processing engines

We study the problem of load balancing in distributed stream processing engines, which is exacerbated in the presence of skew. We introduce Partial Key Grouping (PKG), a new stream partitioning scheme that adapts the classical “power of two choices” to a distributed streaming setting by leveraging two novel techniques: key splitting and local load estimation. In so doing, it achieves better load balancing than key grouping while being more scalable than shuffle grouping. We test PKG on several large datasets, both real-world and synthetic. Compared to standard hashing, PKG reduces the load imbalance by up to several orders of magnitude, and often achieves nearly-perfect load balance. This result translates into an improvement of up to 60% in throughput and up to 45% in latency when deployed on a real Storm cluster.
Self-organized structures in a superorganism : do ants “ behave ” like molecules ?

While the striking structures (e.g. nest architecture, trail networks) of insect societies may seem familiar to many of us, the understanding of pattern formation still constitutes a challenging problem. Over the last two decades, self-organization has dramatically changed our view on how collective decision-making and structures may emerge out of a population of ant workers having each their own individuality as well as a limited access to information. A variety of collective behaviour spontaneously outcome from multiple interactions between nestmates, even when there is no directing influence imposed by an external template, a pacemaker or a leader. By focussing this review on foraging structures, we show that ant societies display some properties which are usually considered in physico-chemical systems, as typical signatures of self-organization. We detail the key role played by feed-back loops, fluctuations, number of interacting units and sensitivity to environmental factors in the emergence of a structured collective behaviour. Nonetheless, going beyond simple analogies with non-living self-organized patterns, we stress on the specificities of social structures made of complex living units of which the biological features have been selected throughout the evolution depending on their adaptive value. In particular, we consider the ability of each ant individual to process information about environmental and social parameters, to accordingly tune its interactions with nestmates and ultimately to determine the final pattern emerging at the collective level. We emphasize on the parsimony and simplicity of behavioural rules at the individual level which allow an efficient processing of information, energy and matter within the whole colony.
Learning real-time MRF inference for image denoising

Many computer vision problems can be formulated in a Bayesian framework with Markov Random Field (MRF) or Conditional Random Field (CRF) priors. Usually, the model assumes that a full Maximum A Posteriori (MAP) estimation will be performed for inference, which can be really slow in practice. In this paper, we argue that through appropriate training, a MRF/CRF model can be trained to perform very well on a suboptimal inference algorithm. The model is trained together with a fast inference algorithm through an optimization of a loss function on a training set containing pairs of input images and desired outputs. A validation set can be used in this approach to estimate the generalization performance of the trained system. We apply the proposed method to an image denoising application, training a Fields of Experts MRF together with a 1-4 iteration gradient descent inference algorithm. Experimental validation on unseen data shows that the proposed training approach obtains an improved benchmark performance as well as a 1000-3000 times speedup compared to the Fields of Experts MRF trained with contrastive divergence. Using the new approach, image denoising can be performed in real-time, at 8 fps on a single CPU for a 256 × 256 image sequence, with close to state-of-the-art accuracy.
Modeling ETL activities as graphs

Extraction-Transformation-Loading (ETL) tools are pieces of software responsible for the extraction of data from several sources, their cleansing, customization and insertion into a data warehouse. In this paper, we focus on the logical design of the ETL scenario of a data warehouse. Based on a formal logical model that includes the data stores, activities and their constituent parts, we model an ETL scenario as a graph, which we call the Architecture Graph. We model all the aforementioned entities as nodes and four different kinds of relationships (instance-of, part-of, regulator and provider relationships) as edges. In addition, we provide simple graph transformations that reduce the complexity of the graph. Finally, in order to support the engineering of the design and the evolution of the warehouse, we introduce specific importance metrics, namely dependence and responsibility, to measure the degree to which entities are bound to each other.
Compatibility Is Not Transparency: VMM Detection Myths and Realities

Recent work on applications ranging from realistic honeypots to stealthier rootkits has speculated about building transparent VMMs – VMMs that are indistinguishable from native hardware, even to a dedicated adversary. We survey anomalies between real and virtual hardware and consider methods for detecting such anomalies, as well as possible countermeasures. We conclude that building a transparent VMM is fundamentally infeasible, as well as impractical from a performance and engineering standpoint.
Evasion and Hardening of Tree Ensemble Classifiers

Classifier evasion consists in finding for a given instance x the “nearest” instance x′ such that the classifier predictions of x and x′ are different. We present two novel algorithms for systematically computing evasions for tree ensembles such as boosted trees and random forests. Our first algorithm uses a Mixed Integer Linear Program solver and finds the optimal evading instance under an expressive set of constraints. Our second algorithm trades off optimality for speed by using symbolic prediction, a novel algorithm for fast finite differences on tree ensembles. On a digit recognition task, we demonstrate that both gradient boosted trees and random forests are extremely susceptible to evasions. Finally, we harden a boosted tree model without loss of predictive accuracy by augmenting the training set of each boosting round with evading instances, a technique we call adversarial boosting.
RECENT DEVELOPMENTS IN PAPER CURRENCY RECOGNITION SYSTEM

Currency denomination recognition is one the active research topics at present. And this wide interest is particularly due to the various potential applications it has. Monetary transaction is an integral part of our day to day activities. However, blind people particularly suffer in monetary transactions. They are not able to effectively distinguish between various denominations and are often deceived by other people. Also, a reliable currency recognition system could be used in any sector wherever monetary transaction is of concern. Thus, there is an ardent need to design a system that is helpful in recognition of paper currency notes correctly. Currency denomination detection is a vast area of research and significant progress had been achieved over the years. This paper presents an extensive survey of research on various developments in recent years in identification of currency denomination. A number of techniques applied by various researchers are discussed briefly in order to assess the state of art.