nils-herrmann/clean_scirepeval huggingface dataset:
Automatic Image Annotation using Deep Learning Representations

We propose simple and effective models for the image annotation that make use of Convolutional Neural Network (CNN) features extracted from an image and word embedding vectors to represent their associated tags. Our first set of models is based on the Canonical Correlation Analysis (CCA) framework that helps in modeling both views - visual features (CNN feature) and textual features (word embedding vectors) of the data. Results on all three variants of the CCA models, namely linear CCA, kernel CCA and CCA with k-nearest neighbor (CCA-KNN) clustering, are reported. The best results are obtained using CCA-KNN which outperforms previous results on the Corel-5k and the ESP-Game datasets and achieves comparable results on the IAPRTC-12 dataset. In our experiments we evaluate CNN features in the existing models which bring out the advantages of it over dozens of handcrafted features. We also demonstrate that word embedding vectors perform better than binary vectors as a representation of the tags associated with an image. In addition we compare the CCA model to a simple CNN based linear regression model, which allows the CNN layers to be trained using back-propagation.
Neural Machine Translation for Query Construction and Composition

Research on question answering with knowledge base has recently seen an increasing use of deep architectures. In this extended abstract, we study the application of the neural machine translation paradigm for question parsing. We employ a sequence-to-sequence model to learn graph patterns in the SPARQL graph query language and their compositions. Instead of inducing the programs through question-answer pairs, we expect a semi-supervised approach, where alignments between questions and queries are built through templates. We argue that the coverage of language utterances can be expanded using late notable works in natural language generation.
Comparative Evaluation of Big-Data Systems on Scientific Image Analytics Workloads

Education University of Washington, School of Computer Science & Engineering Seattle, WA 2017,  Magdalena Balazinska Illinois Institute of Technology, Department of Computer Science Chicago, IL 2015, PhD, committee: Ioan Raicu, Zhiling Lan, Xian-He Sun Emory University, Department of Mathematics & Computer Science Atlanta, GA 2008, MS, advisor: James J. Lu Katholieke Universiteit Leuven, Faculty of Engineering Science Belgium 2005, Diploma cum laude, advisor: Irène Gijbels
Computer vision-based registration techniques for augmented reality

Augmented reality is a term used to describe systems in which computer-generated information is superimposed on top of the real world; for example, through the use of a see-through head-mounted display. A human user of such a system could still see and interact with the real world, but have valuable additional information, such as descriptions of important features or instructions for performing physical tasks, superimposed on the world. For example, the computer could identify objects and overlay them with graphic outlines, labels, and schematics. The graphics are registered to the real-world objects and appear to be “painted” onto those objects. Augmented reality systems can be used to make productivity aids for tasks such as inspection, manufacturing, and navigation. One of the most critical requirements for augmented reality is to recognize and locate real-world objects with respect to the person’s head. Accurate registration is necessary in order to overlay graphics accurately on top of the real-world objects. At the Colorado School of Mines, we have developed a prototype augmented reality system that uses head-mounted cameras and computer vision techniques to accurately register the head to the scene. The current system locates and tracks a set of preplaced passive fiducial targets placed on the real-world objects. The system computes the pose of the objects and displays graphics overlays using a see-through head-mounted display. This paper describes the architecture of the system and outlines the computer vision techniques used.
The Design and Implementation of the Clouds Distributed Operating System

Clouds is a native operating system for a distributed environment. The Clouds operating system is built on top of a kernel called Ra. Ra ís a second generation kernel derived from our experience with the first version of the Clouds operating system. Rø is a minimal, flexible kernel that provides a framework for implementing a variety of distributed operating systems. This paper presents the Clouds paradigm and a brief overview of its first implementation. We then present the details of the Rø kernel, the rationale for its design, and the system services that constitute the Clouds operating system. This work was funded by NSF grant CCR-8619886.
Projection target-specific action of nicotine in the caudal nucleus of the solitary tract.

The brainstem nucleus of the solitary tract (NTS) is the key integrating relay in the central processing of sensory information from the thoracic and from most subdiaphragmatic viscera. Modulation of neuronal excitability and synaptic activity in the NTS by nicotinic agents can have potent effects on vital physiological functions, such as feeding, digestion, respiration, and blood circulation. Caudal NTS neurons demonstrate considerable heterogeneity in projection targets, synaptic properties, and expression of nicotinic acetylcholine receptors (nAChRs). However, despite its heterogeneity, the caudal NTS may contain discrete subsets of neurons with unique projection target-specific properties. To test this hypothesis, we used in vivo fluorescent tracing and ex vivo patch-clamp electrophysiology to evaluate responsiveness to nicotine of anatomically identified caudal NTS neurons that project to the hypothalamic paraventricular nucleus (PVN) and the brainstem caudal ventrolateral medulla (CVLM). The results of this study demonstrate that responsiveness to nicotine correlates with where the neurons project. Specifically, PVN-projecting caudal NTS neurons respond to nicotine only presynaptically (i.e., via activation of presynaptic nAChRs and potentiation of synaptic release of glutamate), suggesting indirect, glutamate-dependent effects of nicotine on the PVN-projecting NTS circuitry. By contrast, CVLM-projecting caudal NTS neurons exhibit only limited presynaptic, but dominant somatodendritic, responsiveness to nicotine, suggesting that the effects of nicotine on the CVLM-projecting NTS circuitry are direct and largely glutamate independent. Understanding the relationships among function-specific brainstem/hypothalamic neuronal networks, nuclei, and individual neurons could help develop therapies targeting identifiable neuronal circuits to offset impaired autonomic homeostasis.
Dynamics simulation toolbox for industrial robot manipulators

A new robot toolbox for dynamics simulation based on MATLAB Graphical User Interface (GUI) is developed for educational purposes. It is built on the previous version named as ROBOLAB performing only the kinematics analysis of robot manipulators. The toolbox presented in this paper provides interactive real-time simulation and visualization of the industrial robot manipulator’s dynamics based on Langrange Euler and Newton Euler formulations. Since most of the industrial robot manipulators are produced with six degrees of freedom (DOF) such as the PUMA 560, the Fanuc ArcMate 120iB and Stanford Arm, the library of the toolbox includes sixteen fundamental 6-DOF robot manipulators with Euler wrist. The software can be used to interactively perform robotics analysis and off-line programming of robot dynamics such as forward and inverse dynamics as well as to interactively teach and simulate the basic principles of robot dynamics in a very realistic way. In order to demonstrate the user-friendly features of the toolbox much better, simulation of the NS robot manipulator (Stanford Arm) is provided as an example. 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 18: 319 330, 2010; ; DOI 10.1002/cae.20262
Efficient Ordered Combinatorial Semi-Bandits for Whole-Page Recommendation

Multi-Armed Bandit (MAB) framework has been successfully applied in many web applications. However, many complex real-world applications that involve multiple content recommendations cannot fit into the traditional MAB setting. To address this issue, we consider an ordered combinatorial semi-bandit problem where the learner recommends S actions from a base set of K actions, and displays the results in S (out of M ) different positions. The aim is to maximize the cumulative reward with respect to the best possible subset and positions in hindsight. By the adaptation of a minimumcost maximum-flow network, a practical algorithm based on Thompson sampling is derived for the (contextual) combinatorial problem, thus resolving the problem of computational intractability. With its potential to work with whole-page recommendation and any probabilistic models, to illustrate the effectiveness of our method, we focus on Gaussian process optimization and a contextual setting where click-throughrate is predicted using logistic regression. We demonstrate the algorithms’ performance on synthetic Gaussian process problems and on large-scale news article recommendation datasets from Yahoo! Front Page Today Module.
A fuzzy-genetic approach to breast cancer diagnosis

The automatic diagnosis of breast cancer is an important, real-world medical problem. In this paper we focus on the Wisconsin breast cancer diagnosis (WBCD) problem, combining two methodologies-fuzzy systems and evolutionary algorithms-so as to automatically produce diagnostic systems. We find that our fuzzy-genetic approach produces systems exhibiting two prime characteristics: first, they attain high classification performance (the best shown to date), with the possibility of attributing a confidence measure to the output diagnosis; second, the resulting systems involve a few simple rules, and are therefore (human-) interpretable.
VSkin: Sensing Touch Gestures on Surfaces of Mobile Devices Using Acoustic Signals

Enabling touch gesture sensing on all surfaces of the mobile device, not limited to the touchscreen area, leads to new user interaction experiences. In this paper, we propose VSkin, a system that supports fine-grained gesture-sensing on the back of mobile devices based on acoustic signals. VSkin utilizes both the structure-borne sounds, i.e., sounds propagating through the structure of the device, and the air-borne sounds, i.e., sounds propagating through the air, to sense finger tapping and movements. By measuring both the amplitude and the phase of each path of sound signals, VSkin detects tapping events with an accuracy of 99.65% and captures finger movements with an accuracy of 3.59mm.
A Fast Parallel Clustering Algorithm for Large Spatial Databases

The clustering algorithm DBSCAN relies on a density-based notion of clusters and is designed to discover clusters of arbitrary shape as well as to distinguish noise. In this paper, we present PDBSCAN, a parallel version of this algorithm. We use the ‘shared-nothing’ architecture with multiple computers interconnected through a network. A fundamental component of a shared-nothing system is its distributed data structure. We introduce the dR*-tree, a distributed spatial index structure in which the data is spread among multiple computers and the indexes of the data are replicated on every computer. We implemented our method using a number of workstations connected via Ethernet (10 Mbit). A performance evaluation shows that PDBSCAN offers nearly linear speedup and has excellent scaleup and sizeup behavior.
Large air gap coupler for inductive charger

A novel magnetic coupler of large r r gap IS presented It IS developed for the eleehlo relucle s aumahc mduchve charger The ncw induchve coupler proposed hqe has sUmcient mung ~oducmnce ven rf I has a large a s gap Calculated exrung inductance 18 40 pH at 2 nuns of w d m g and 5 mm ay gap, whch apees well with measured value In order to assess the gmerahon of heat c w e d by the eddy cment, the magneuc flux densrhes in the lnduchve chaser and also a flat non plate, to wbch the inductive charger IS attached, Bre calculated The conyersion efXciency wth the coupler and a MOSFETs full-bndge inverier of IW lrHq IS 97% at 8 3 kW output
Generating and Adapting Game Mechanics

Game designs often center on the game mechanics—rules governing the logical evolution of the game. We seek to develop an intelligent system that generates computer games and assists humans in designing games. As first steps towards this goal we present a composable and cross-domain representation for game mechanics that draws from AI planning action representations. We use a constraint solver to generate mechanics subject to design requirements on the form of those mechanics—what they do in the game. A planner takes a set of generated mechanics and tests whether those mechanics meet playability requirements—controlling how mechanics function in a game to affect player behavior. We demonstrate our system by modeling and generating mechanics in a role-playing game, platformer game, and combined role-playing-platformer game.
Comparing human perceptions of post-editing effort with post-editing operations

Post-editing performed by translators is an increasingly common use of machine translated texts. While high quality MT may increase productivity, post-editing poor translations can be a frustrating task which requires more effort than translating from scratch. For this reason, estimating whether machine translations are of sufficient quality to be used for post-editing and finding means to reduce post-editing effort are an important field of study. Post-editing effort consists of different aspects, of which temporal effort, or the time spent on post-editing, is the most visible and involves not only the technical effort needed to perform the editing, but also the cognitive effort required to detect and plan necessary corrections. Cognitive effort is difficult to examine directly, but ways to reduce the cognitive effort in particular may prove valuable in reducing the frustration associated with postediting work. In this paper, we describe an experiment aimed at studying the relationship between technical post-editing effort and cognitive post-editing effort by comparing cases where the edit distance and a manual score reflecting perceived effort differ. We present results of an error analysis performed on such sentences and discuss the clues they may provide about edits requiring great cognitive effort compared to the technical effort, on one hand, or little cognitive effort, on the other.
Iris Recognition: On the Segmentation of Degraded Images Acquired in the Visible Wavelength

Iris recognition imaging constraints are receiving increasing attention. There are several proposals to develop systems that operate in the visible wavelength and in less constrained environments. These imaging conditions engender acquired noisy artifacts that lead to severely degraded images, making iris segmentation a major issue. Having observed that existing iris segmentation methods tend to fail in these challenging conditions, we present a segmentation method that can handle degraded images acquired in less constrained conditions. We offer the following contributions: 1) to consider the sclera the most easily distinguishable part of the eye in degraded images, 2) to propose a new type of feature that measures the proportion of sclera in each direction and is fundamental in segmenting the iris, and 3) to run the entire procedure in deterministically linear time in respect to the size of the image, making the procedure suitable for real-time applications.
Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders

Computer-aided diagnosis (CAD) is a promising tool for accurate and consistent diagnosis and prognosis. Cell detection and segmentation are essential steps for CAD. These tasks are challenging due to variations in cell shapes, touching cells, and cluttered background. In this paper, we present a cell detection and segmentation algorithm using the sparse reconstruction with trivial templates and a stacked denoising autoencoder (sDAE). The sparse reconstruction handles the shape variations by representing a testing patch as a linear combination of shapes in the learned dictionary. Trivial templates are used to model the touching parts. The sDAE, trained with the original data and their structured labels, is used for cell segmentation. To the best of our knowledge, this is the first study to apply sparse reconstruction and sDAE with structured labels for cell detection and segmentation. The proposed method is extensively tested on two data sets containing more than 3000 cells obtained from brain tumor and lung cancer images. Our algorithm achieves the best performance compared with other state of the arts.
Cleanroom Software Engineering for Zero-Defect Software

Cleanroom software engineering is a theory-based, team-oriented process for developing very high quality software under statistical quality control. Cleanroom combines formal methods of object-based box structure specification and design, functiontheoretic correctness veri/ication, and statistical usage testing for quality certification, to produce sofmare that is zero defects with high probability. Cleanroom management is based on a l fe cycle of incremental development of userfunction sofmare increments that accumulate into the PnaI product. Cleanroom teams in IBM and other organizations are achieving remarkable quality results in both new system development and modifications and extensions to existing systems.
A survey of top-k query processing techniques in relational database systems

Efficient processing of top-k queries is a crucial requirement in many interactive environments that involve massive amounts of data. In particular, efficient top-k processing in domains such as the Web, multimedia search, and distributed systems has shown a great impact on performance. In this survey, we describe and classify top-k processing techniques in relational databases. We discuss different design dimensions in the current techniques including query models, data access methods, implementation levels, data and query certainty, and supported scoring functions. We show the implications of each dimension on the design of the underlying techniques. We also discuss top-k queries in XML domain, and show their connections to relational approaches.
Chemical Composition, Antibacterial and Antioxidant Activities of Six Essentials Oils from the Alliaceae Family

Six essential oils (EOs) from the Alliaceae family, namely garlic (Allium sativum), onion (Allium cepa), leek (Allium porrum), Chinese chive (Allium tuberosum), shallot (Allium ascalonicum) and chive (Allium schoenoprasum) were characterized by GC and GC-MS and evaluated for their functional food properties. Antibacterial properties were tested on five food-borne pathogens: Two Gram-positive Staphylococcus aureus (ATCC 25923), Listeria monocytogenes (ATCC 19115) and three Gram-negative Salmonella Typhimurium (ATCC 14028), Escherichia coli (ATCC 8739) and Campylobacter jejuni (ATCC 33291) bacteria. Antioxidant and radical-scavenging properties were tested by means of Folin-Ciocalteu and 2,2-diphenyl-1-picrylhydrazyl (DPPH) assays. Garlic, Chinese chive and onion EOs had the highest antibacterial activity whereas shallot and leek EOs were the strongest antioxidants. Heating caused a decrease in the antioxidant activity of these Eos, as shown in the Total Polar Materials (TPM) test. Suggestions on relationships between chemical composition and biological activities are presented. Results show that the EOs could be of value in the food industry as alternatives to synthetic antioxidants.
Moving shadow detection and removal - a wavelet transform based approach

Shadow detection and removal is an important problem in computer vision. The real challenge in moving shadow detection and removal is to classify moving shadow points which are many times misclassified as moving object points in a video sequences. Various shadow detection and removal algorithms have been proposed for images but only a few works have been done for moving objects. In this study, a novel method for shadow detection and removal is proposed using discrete wavelet transform (DWT). The authors have used DWT because of its multi-resolution property that decomposes an image into four different bands without loss of the spatial information. For detection and removal of shadow, they have proposed a new threshold in the form of relative standard deviation. The value of threshold is automatically determined and does not require any supervised learning or manual calibration. The proposed method is flexible and depends on only one parameter, namely, wavelet coefficients. Results of shadow detection and removal from moving object after applying the proposed method are compared with the results of other state-of-the-art methods in terms of visual performance and a number of quantitative performance parameters. The proposed method is found to be better and more robust than other methods.
Hierarchical framework for direct gradient-based time-to-contact estimation

The time-to-contact (TTC) estimation is a simple and convenient way to detect approaching objects, potential danger, and to analyze surrounding environment. TTC can be estimated directly from single camera though neither distance nor speed information can be estimated with single cameras. Traditional TTC estimation depends on “interesting feature points” or object boundaries, which is noisy and time consuming. In [13], we propose a direct “gradient-based” method to compute time-to-contact in three special cases that avoid feature points/lines and can take advantages of all related pixels for better computation. In this follow-up paper, we discuss the method to deal with the most general cases and propose a hierarchical fusion framework for direct gradient-based time-to-contact estimation. The new method enhances accuracy, robustness and is computationally efficient, which is important to provide fast response for vehicle applications.
A Blockchain-Based Micro Economy Platform for Distributed Infrastructure Initiatives

Distributed Infrastructure Initiatives (DIIs) are communities that collaboratively produce and consume infrastructure. To develop a healthy ecosystem, DIIs require an economic model that balances supply and demand, but there is currently a lack of tooling to support implementing these. In this research, we propose an architecture for a platform that enables DIIs to implement such models, focused around a digital currency based on blockchain technology. The currency is issued according to the amount participants contribute to the initiative, which is quantified based on operational metrics gathered from the infrastructure. Furthermore, the platform enables participants to deploy smart contracts which encode self-enforcing agreements about the infrastructure services they exchange. The architecture has been evaluated through a case study at The Things Network (TTN) a global distributed crowdsourced Internet of Things initiative. The case study revealed that the architecture is effective for the selected case at TTN. In addition, the results motivate future research lines to support scalability (i.e., to deploy the architecture on a larger scale) and security.