nils-herrmann/clean_scirepeval huggingface dataset:
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting "spatial pyramid" is a simple and computationally efficient extension of an orderless bag-of-features image representation, and it shows significantly improved performance on challenging scene categorization tasks. Specifically, our proposed method exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories. The spatial pyramid framework also offers insights into the success of several recently proposed image descriptions, including Torralba’s "gist" and Lowe’s SIFT descriptors.
Feminism and interaction design

This workshop is aimed at exploring the issues at the intersection of feminist thinking and human computer interaction. Both feminism and HCI have made important contributions to social science in the past several decades, but though their potential for overlap seem high, they have not engaged each other directly until recently. In this workshop we will explore diverse--and contentious--ways that feminist perspectives can support user research, design ideation and problem framing, sketching and prototyping, and design criticism and evaluation. The workshop will include fast-moving mini-panels and hands-on group exercises emphasizing feminist interaction criticism and design ideation.
45° Linearly Polarized Resonant Slot Array Antenna Based on Substrate Integrated Waveguide

In this paper, a new configuration of integrated 45deg linearly polarized slot array antenna based on substrate integrated waveguide (SIW) technology is proposed, designed and fabricated with normal printed circuit board (PCB) process. The antenna consists of eight 45deg inclined slots and the measured gain is higher than 10.27 dBi at X-band. The measured radiation patterns and the return loss are in agreement with the simulated results.
Informative Path Planning and Mapping with Multiple UAVs in Wind Fields

Informative path planning (IPP) is used to design paths for robotic sensor platforms to extract the best/maximum possible information about a quantity of interest while operating under a set of constraints, such as dynamic feasibility of vehicles. The key challenges of IPP are the strong coupling in multiple layers of decisions: the selection of locations to visit, the allocation of sensor platforms to those locations; and the processing of the gathered information along the paths. This paper presents an systematic procedure for IPP and environmental mapping using multiple UAV sensor platforms. It (a) selects the best locations to observe, (b) calculates the cost and finds the best paths for each UAV, and (c) estimates the measurement value within a given region using the Gaussian process (GP) regression framework. An illustrative example of RF intensity field mapping is presented to demonstrate the validity and applicability of the proposed approach.
YAGO: A Large Ontology from Wikipedia and WordNet

This article presents YAGO, a large ontology with high coverage and precision. YAGO has been automatically derived from Wikipedia and WordNet. It comprises entities and relations, and currently contains more than 1.7 million entities and 15 million facts. These include the taxonomic Is-A hierarchy as well as semantic relations between entities. The facts for YAGO have been extracted from the category system and the infoboxes of Wikipedia and have been combined with taxonomic relations from WordNet. Type Ontologies Information extraction K checking techniques help us keep YAGO’s precision at 95%—as proven by an extensive evaluation study. YAGO is based on a clean logical model with a decidable consistency. Furthermore, it allows representing n-ary relations in a natural way while maintaining compatibility with RDFS. A powerful query model s data
Where to Buy It: Matching Street Clothing Photos in Online Shops

In this paper, we define a new task, Exact Street to Shop, where our goal is to match a real-world example of a garment item to the same item in an online shop. This is an extremely challenging task due to visual differences between street photos (pictures of people wearing clothing in everyday uncontrolled settings) and online shop photos (pictures of clothing items on people, mannequins, or in isolation, captured by professionals in more controlled settings). We collect a new dataset for this application containing 404,683 shop photos collected from 25 different online retailers and 20,357 street photos, providing a total of 39,479 clothing item matches between street and shop photos. We develop three different methods for Exact Street to Shop retrieval, including two deep learning baseline methods, and a method to learn a similarity measure between the street and shop domains. Experiments demonstrate that our learned similarity significantly outperforms our baselines that use existing deep learning based representations.
Source Code Interaction on Touchscreens

Direct interaction with touchscreens has become a primary way of using a device. This work seeks to devise interaction methods for editing textual source code on touch-enabled devices. With the advent of the “Post-PC Era”, touch-centric interaction has received considerable attention in both research and development. However, various limitations have impeded widespread adoption of programming environments on modern platforms. Previous attempts have mainly been successful by simplifying or constraining conventional programming but have only insufficiently supported source code written in mainstream programming languages. This work includes the design, development, and evaluation of techniques for editing, selecting, and creating source code on touchscreens. The results contribute to text editing and entry methods by taking the syntax and structure of programming languages into account while exploiting the advantages of gesture-driven control. Furthermore, this work presents the design and software architecture of a mobile development environment incorporating touch-enabled modules for typical software development tasks.
Structured Streaming: A Declarative API for Real-Time Applications in Apache Spark

With the ubiquity of real-time data, organizations need streaming systems that are scalable, easy to use, and easy to integrate into business applications. Structured Streaming is a new high-level streaming API in Apache Spark based on our experience with Spark Streaming. Structured Streaming differs from other recent streaming APIs, such as Google Dataflow, in two main ways. First, it is a purely declarative API based on automatically incrementalizing a static relational query (expressed using SQL or DataFrames), in contrast to APIs that ask the user to build a DAG of physical operators. Second, Structured Streaming aims to support end-to-end real-time applications that integrate streaming with batch and interactive analysis. We found that this integration was often a key challenge in practice. Structured Streaming achieves high performance via Spark SQL's code generation engine and can outperform Apache Flink by up to 2x and Apache Kafka Streams by 90x. It also offers rich operational features such as rollbacks, code updates, and mixed streaming/batch execution. We describe the system's design and use cases from several hundred production deployments on Databricks, the largest of which process over 1 PB of data per month.
Person identification from the EEG using nonlinear signal classification.

This paper focusses on the person identification problem based on features extracted from the ElectroEncephaloGram (EEG). A bilinear rather than a purely linear model is fitted on the EEG signal, prompted by the existence of non-linear components in the EEG signal--a conjecture already investigated in previous research works. The novelty of the present work lies in the comparison between the linear and the bilinear results, obtained from real field EEG data, aiming towards identification of healthy subjects rather than classification of pathological cases for diagnosis.



The EEG signal of a, in principle, healthy individual is processed via (non)linear (AR, bilinear) methods and classified by an artificial neural network classifier.



Experiments performed on real field data show that utilization of the bilinear model parameters as features improves correct classification scores at the cost of increased complexity and computations. Results are seen to be statistically significant at the 99.5% level of significance, via the chi 2 test for contingency.



The results obtained in the present study further corroborate existing research, which shows evidence that the EEG carries individual-specific information, and that it can be successfully exploited for purposes of person identification and authentication.
A Probabilistic Approach to Collaborative Multi-Robot Localization

This paper presents a statistical algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot’s belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The technique has been implemented and tested using two mobile robots equipped with cameras and laser range-finders for detecting other robots. The results, obtained with the real robots and in series of simulation runs, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization. A further experiment demonstrates that under certain conditions, successful localization is only possible if teams of heterogeneous robots collaborate during localization.
PEMOGEN: Automatic adaptive performance modeling during program runtime

Traditional means of gathering performance data are tracing, which is limited by the available storage, and profiling, which has limited accuracy. Performance modeling is often used to interpret the tracing data and generate performance predictions. We aim to complement the traditional data collection mechanisms with online performance modeling, a method that generates performance models while the application is running. This allows us to greatly reduce the storage overhead while still producing accurate predictions. We present PEMOGEN, our compilation and modeling framework that automatically instruments applications to generate performance models during program execution. We demonstrate the ability of PEMOGEN to both reduce storage cost and improve the prediction accuracy compared to traditional techniques such as least squares fitting. With our tool, we automatically detect 3,370 kernels from fifteen NAS and Mantevo applications and model their execution time with a median coefficient of variation R2 of 0.81. These automatically generated performance models can be used to quickly assess the scaling and potential bottlenecks with regards to any input parameter and the number of processes of a parallel application.
Determining Success for Different Website Goals

In building a successful website it is imperative that the design matches the organization’s objectives, which should be well defined. Clearly, different types of websites will have different goals. Thus, determining success across websites is both goal and context specific. One measure of website success is satisfaction, and the resulting intent to return to a website. Built on theories from the IS success and information technology adoption literatures, this study investigated four variables believed to impact website satisfaction: information quality, system quality, perceived effectiveness, and social influence. Data was collected by surveying regular users of two different websites, each from a different category within a taxonomy of websites. Structural equation modeling techniques were used to test the proposed model of website success for each studied site. The results indicate that the determinants of satisfaction and overall successfulness of websites are both context dependent and goal specific.
Corporate Governance and Equity Prices July 2001

Corporate-governance provisions related to takeover defenses and shareholder rights vary substantially across firms. In this paper, we use the incidence of 24 different provisions to build a “Governance Index” for about 1,500 firms per year, and then we study the relationship between this index and several forward-looking performance measures during the 1990s. We find a striking relationship between corporate governance and stock returns. An investment strategy that bought the firms in the lowest decile of the index (strongest shareholder rights) and sold the firms in the highest decile of the index (weakest shareholder rights) would have earned abnormal returns of 8.5 percent per year during the sample period. Furthermore, the Governance Index is highly correlated with firm value. In 1990, a one-point increase in the index is associated with a 2.4 percentage-point lower value for Tobin’s Q. By 1999, this difference had increased significantly, with a one-point increase in the index associated with an 8.9 percentagepoint lower value for Tobin’s Q. Finally, we find that weaker shareholder rights are associated with lower profits, lower sales growth, higher capital expenditures, and a higher amount of corporate acquisitions. We conclude with a discussion of several causal interpretations.
A Comparative Look into Public IXP Datasets

Internet eXchange Points (IXPs) are core components of the Internet infrastructure where Internet Service Providers (ISPs) meet and exchange traffic. During the last few years, the number and size of IXPs have increased rapidly, driving the flattening and shortening of Internet paths. However, understanding the present status of the IXP ecosystem and its potential role in shaping the future Internet requires rigorous data about IXPs, their presence, status, participants, etc. In this work, we do the first cross-comparison of three well-known publicly available IXP databases, namely of PeeringDB, Euro-IX, and PCH. A key challenge we address is linking IXP identifiers across databases maintained by different organizations. We find different AS-centric versus IXP-centric views provided by the databases as a result of their data collection approaches. In addition, we highlight differences and similarities w.r.t. IXP participants, geographical coverage, and co-location facilities. As a side-product of our linkage heuristics, we make publicly available the union of the three databases, which includes 40.2% more IXPs and 66.3% more IXP participants than the commonly-used PeeringDB. We also publish our analysis code to foster reproducibility of our experiments and shed preliminary insights into the accuracy of the union dataset.
Point Precisely: Towards Ensuring the Precision of Data in Generated Texts Using Delayed Copy Mechanism

The task of data-to-text generation aims to generate descriptive texts conditioned on a number of database records, and recent neural models have shown significant progress on this task. The attention based encoder-decoder models with copy mechanism have achieved state-of-the-art results on a few data-to-text datasets. However, such models still face the problem of putting incorrect data records in the generated texts, especially on some more challenging datasets like ROTOWIRE. In this paper, we propose a two-stage approach with a delayed copy mechanism to improve the precision of data records in the generated texts. Our approach first adopts an encoder-decoder model to generate a template text with data slots to be filled and then leverages a proposed delayed copy mechanism to fill in the slots with proper data records. Our delayed copy mechanism can take into account all the information of the input data records and the full generated template text by using double attention, position-aware attention and a pairwise ranking loss. The two models in the two stages are trained separately. Evaluation results on the ROTOWIRE dataset verify the efficacy of our proposed approach to generate better templates and copy data records more precisely.
Natural-Parameter Networks: A Class of Probabilistic Neural Networks

Neural networks (NN) have achieved state-of-the-art performance in various applications. Unfortunately in applications where training data is insufficient, they are often prone to overfitting. One effective way to alleviate this problem is to exploit the Bayesian approach by using Bayesian neural networks (BNN). Another shortcoming of NN is the lack of flexibility to customize different distributions for the weights and neurons according to the data, as is often done in probabilistic graphical models. To address these problems, we propose a class of probabilistic neural networks, dubbed natural-parameter networks (NPN), as a novel and lightweight Bayesian treatment of NN. NPN allows the usage of arbitrary exponential-family distributions to model the weights and neurons. Different from traditional NN and BNN, NPN takes distributions as input and goes through layers of transformation before producing distributions to match the target output distributions. As a Bayesian treatment, efficient backpropagation (BP) is performed to learn the natural parameters for the distributions over both the weights and neurons. The output distributions of each layer, as byproducts, may be used as second-order representations for the associated tasks such as link prediction. Experiments on real-world datasets show that NPN can achieve state-of-the-art performance.
Plant Disease Prediction using Image Processing Techniques-A Review

Agriculture has become much more than simply a means to feed ever growing populations. It is very important where in more than 70% population depends on agriculture in India. That means it feeds great number of people. The plant diseases effect the humans directly or indirectly by health or also economically. To detect these plant diseases we need a fast automatic way. Diseases are analyzed by different digital image processing techniques. In this paper, we have done survey on different digital image processing techniques to detect the plant diseases.
Sensing and reacting to microbes through the inflammasomes

Inflammasomes are multiprotein complexes that activate caspase-1, which leads to maturation of the proinflammatory cytokines interleukin 1β (IL-1β) and IL-18 and the induction of pyroptosis. Members of the Nod-like receptor (NLR) family, including NLRP1, NLRP3 and NLRC4, and the cytosolic receptor AIM2 are critical components of inflammasomes and link microbial and endogenous danger signals to the activation of caspase-1. In response to microbial infection, activation of the inflammasomes contributes to host protection by inducing immune responses that limit microbial invasion, but deregulated activation of inflammasomes is associated with autoinflammatory syndromes and other pathologies. Thus, understanding inflammasome pathways may provide insight into the mechanisms of host defense against microbes and the development of inflammatory disorders.
Modeling Ambiguity, Subjectivity, and Diverging Viewpoints in Opinion Question Answering Systems

Product review websites provide an incredible lens into the wide variety of opinions and experiences of different people, and play a critical role in helping users discover products that match their personal needs and preferences. To help address questions that can't easily be answered by reading others' reviews, some review websites also allow users to pose questions to the community via a question-answering (QA) system. As one would expect, just as opinions diverge among different reviewers, answers to such questions may also be subjective, opinionated, and divergent. This means that answering such questions automatically is quite different from traditional QA tasks, where it is assumed that a single 'correct' answer is available. While recent work introduced the idea of question-answering using product reviews, it did not account for two aspects that we consider in this paper: (1) Questions have multiple, often divergent, answers, and this full spectrum of answers should somehow be used to train the system, and (2) What makes a 'good' answer depends on the asker and the answerer, and these factors should be incorporated in order for the system to be more personalized. Here we build a new QA dataset with 800 thousand questions—and over 3.1 million answers—and show that explicitly accounting for personalization and ambiguity leads both to quantitatively better answers, but also a more nuanced view of the range of supporting, but subjective, opinions.
Correction to 'Neural systems for landmark-based wayfinding in humans'.

Supplementary data ml http://rstb.royalsocietypublishing.org/content/suppl/2014/02/10/rstb.2012.0533.DC1.ht "Audio Supplement" References http://rstb.royalsocietypublishing.org/content/369/1635/20120533.full.html#ref-list-1 This article cites 54 articles, 19 of which can be accessed free Subject collections (451 articles) neuroscience   (332 articles) cognition   (510 articles) behaviour   Articles on similar topics can be found in the following collections
Design of a linear variable-stiffness mechanism using preloaded bistable beams

A machine with variable internal stiffness can meet the performance requirement in different working environments. Linear variable-stiffness mechanisms (LVSM) are rarely found compared with those producing rotary motion. This paper presents an approach to design a linear variable-stiffness mechanism. The idea is to parallel connect two lateral bistable beams with a vertical spring. Through the preload adjustment of the bistable beams, the output force to displacement curve can exhibit different stiffnesses. The merit of the proposed LVSM is that very large stiffness variation can be achieved in a compact space. The stiffness may even be tuned to zero by assigning a proper stiffness to the vertical spring. An optimization formulation is presented to design a LVSM with the largest stiffness variation. The effects of various parameters on the stiffness variation and linearity are discussed. The results are numerically verified with a prototype illustrated.
Architectural Mismatch or Why it's hard to build systems out of existing parts

Many would argue that future breakthroughs in software productivity will depend on our ability to combine existing pieces of software to produce new applications. An important step towards this goal is the development of new techniques to detect and cope with mismatches in the assembled parts. Some problems of composition are due to low-level issues of interoperability, such as mismatches in programming languages or database schemas. However, in this paper we highlight a different, and in many ways more pervasive, class of problem: architectural mismatch. Specifically, we use our experience in building a family of software design environments from existing parts to illustrate a variety of types of mismatch that center around the assumptions a reusable part makes about the structure of the application in which is to appear. Based on this experience we show how an architectural view of the mismatch problem exposes some fundamental, thorny problems for software composition and suggests possible research avenues needed to solve them.