nils-herrmann/clean_scirepeval huggingface dataset:
Tree Filtering: Efficient Structure-Preserving Smoothing With a Minimum Spanning Tree

We present a new efficient edge-preserving filter-“tree filter”-to achieve strong image smoothing. The proposed filter can smooth out high-contrast details while preserving major edges, which is not achievable for bilateral-filter-like techniques. Tree filter is a weighted-average filter, whose kernel is derived by viewing pixel affinity in a probabilistic framework simultaneously considering pixel spatial distance, color/intensity difference, as well as connectedness. Pixel connectedness is acquired by treating pixels as nodes in a minimum spanning tree (MST) extracted from the image. The fact that an MST makes all image pixels connected through the tree endues the filter with the power to smooth out high-contrast, fine-scale details while preserving major image structures, since pixels in small isolated region will be closely connected to surrounding majority pixels through the tree, while pixels inside large homogeneous region will be automatically dragged away from pixels outside the region. The tree filter can be separated into two other filters, both of which turn out to have fast algorithms. We also propose an efficient linear time MST extraction algorithm to further improve the whole filtering speed. The algorithms give tree filter a great advantage in low computational complexity (linear to number of image pixels) and fast speed: it can process a 1-megapixel 8-bit image at ~ 0.25 s on an Intel 3.4 GHz Core i7 CPU (including the construction of MST). The proposed tree filter is demonstrated on a variety of applications.
Cell segmentation in microscopy imagery using a bag of local Bayesian classifiers

Cell segmentation in microscopy imagery is essential for many bioimage applications such as cell tracking. To segment cells from the background accurately, we present a pixel classification approach that is independent of cell type or imaging modality. We train a set of Bayesian classifiers from clustered local training image patches. Each Bayesian classifier is an expert to make decision in its specific domain. The decision from the mixture of experts determines how likely a new pixel is a cell pixel. We demonstrate the effectiveness of this approach on four cell types with diverse morphologies under different microscopy imaging modalities.
Similarity of Semantic Relations

There are at least two kinds of similarity. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason:stone is analogous to the pair carpenter:wood. This article introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, and information retrieval. Recently the Vector Space Model (VSM) of information retrieval has been adapted to measuring relational similarity, achieving a score of 47% on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) The patterns are derived automatically from the corpus, (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data, and (3) automatically generated synonyms are used to explore variations of the word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying semantic relations, LRA achieves similar gains over the VSM.
Adaptive integrated CMOS circulator

An adaptive circulator fabricated on a 130 nm CMOS is presented. Circulator has two adaptive blocks for gain and phase mismatch correction and leakage cancelation. The impedance matching circuit corrects mismatches for antenna, divider, and LNTA. The cancelation block cancels the Tx leakage. Measured isolation between transmitter and receiver for single tone at 2.4 GHz is 90 dB, and for a 40 MHz wide-band signal is 50dB. The circulator Rx gain is 10 dB, with NF = 4.7 dB and 5 dB insertion loss.
An Artificial Neural Network for Detection of Simulated Dental Caries

A neural network was developed to diagnose artificial dental caries using images from a charged-coupled device (CCD)camera and intra-oral digital radiography. The diagnostic performance of this neural network was evaluated against a gold standard.  The neural network design was the Learning Vector Quantization (LVQ) used to classify a tooth surface as sound or as having dental caries. The depth of the dental caries was indicated on a graphic user interface (GUI) screen developed by Matlab programming. Forty-nine images of both sound and simulated dental caries, derived from a CCD camera and by digital radiography, were used to ‘train’ an artificial neural network. After the ’training’ process, a separate test-set comprising 322 unseen images was evaluated. Tooth sections and microscopic examinations were used to confirm the actual dental caries status.The performance of neural network was evaluated using diagnostic test.  The sensitivity (95%CI)/specificity (95%CI) of dental caries detection by the CCD camera and digital radiography were 0.77(0.68-0.85)/0.85(0.75-0.92) and 0.81(0.72-0.88)/0.93(0.84-0.97), respectively. The accuracy of caries depth-detection by the CCD camera and digital radiography was 58 and 40%, respectively.  The model neural network used in this study could be a prototype for caries detection but should be improved for classifying caries depth. Our study suggests an artificial neural network can be trained to make the correct interpretations of dental caries.
GESPAR: Efficient Phase Retrieval of Sparse Signals

We consider the problem of phase retrieval, namely, recovery of a signal from the magnitude of its Fourier transform, or of any other linear transform. Due to the loss of Fourier phase information, this problem is ill-posed. Therefore, prior information on the signal is needed in order to enable its recovery. In this work we consider the case in which the signal is known to be sparse, i.e., it consists of a small number of nonzero elements in an appropriate basis. We propose a fast local search method for recovering a sparse signal from measurements of its Fourier transform (or other linear transform) magnitude which we refer to as GESPAR: GrEedy Sparse PhAse Retrieval. Our algorithm does not require matrix lifting, unlike previous approaches, and therefore is potentially suitable for large scale problems such as images. Simulation results indicate that GESPAR is fast and more accurate than existing techniques in a variety of settings.
The most controversial topics in Wikipedia: A multilingual and geographical analysis

We present, visualize and analyse the similarities and differences between the controversial topics related to “edit wars” identified in 10 different language versions of Wikipedia. After a brief review of the related work we describe the methods developed to locate, measure, and categorize the controversial topics in the different languages. Visualizations of the degree of overlap between the top 100 list of most controversial articles in different languages and the content related geographical locations will be presented. We discuss what the presented analysis and visualizations can tell us about the multicultural aspects of Wikipedia, and, in general, about cultures of peer-production with focus on universal and specifically, local features. We demonstrate that Wikipedia is more than just an encyclopaedia; it is also a window into divergent social-spatial priorities, interests and preferences.
Modeling of Permanent Magnet Motor Drives

Recent research has Indicated (hat (he permanent magnet motor drives, which include the permanent magnet synchronous motor (PMSM) and the brushless dc motor (BDCM) could become serious competitors to the induction motor for servo applications. The PMSM has a sinusoidal back emf and requires sinusoidal stator currents to produce constant torque while the BDCM has a trapezoidal back emf and requires rectangular stator currents to produce constant torque. The PMSM is very similar to the wound rotor synchronous machine except that the PMSM that is used for servo applications tends not to have any damper windings and excitation is provided by a permanent magnet instead of a fi~ld winding. Hence the d, q model of the PMSM can be derived from the well-known model of the synchronous machine with the equations of the damper windings and field current dynamics removed. Because of tbe nonsinusoidal variation of the mutual inductances between the stator and rotor in the BDCM, it is also shown In this paper that no particular advantage exists in transforming the abc equations of the BCDM to the d, q frame. Hence the solution of the original abc equations is proposed for the BDCM.
Latent Max-Margin Multitask Learning With Skelets for 3-D Action Recognition

Recent emergence of low-cost and easy-operating depth cameras has reinvigorated the research in skeleton-based human action recognition. However, most existing approaches overlook the intrinsic interdependencies between skeleton joints and action classes, thus suffering from unsatisfactory recognition performance. In this paper, a novel latent max-margin multitask learning model is proposed for 3-D action recognition. Specifically, we exploit skelets as the mid-level granularity of joints to describe actions. We then apply the learning model to capture the correlations between the latent skelets and action classes each of which accounts for a task. By leveraging structured sparsity inducing regularization, the common information belonging to the same class can be discovered from the latent skelets, while the private information across different classes can also be preserved. The proposed model is evaluated on three challenging action data sets captured by depth cameras. Experimental results show that our model consistently achieves superior performance over recent state-of-the-art approaches.
An RFID attendance and monitoring system for university applications

The main objective of this paper is to enhance the university's monitoring system taking into account factors such as reliability, time saving, and easy control. The proposed system consists of a mobile RFID solution in a logical context. The system prototype and its small scale application was a complete success. However, the more practical phase will not be immediately ready because a large setup is required and a part of the existing system has to be completely disabled. Some software modifications in the RFID system can be easily done in order for the system to be ready for a new application. In this paper, advantages and disadvantages of the proposed RFID system will be presented.
Bundling Information Goods : Pricing , Profits and Efficiency

Internet-based technologies such as micropayments increasingly enable the sale and delivery of small units of information. This paper draws attention to the opposite strategy of bundling a large number of information goods, such as those increasingly available on the Internet, for a fixed price that does not depend on how many goods are actually used by the buyer. We analyze the optimal bundling strategies for a multiproduct monopolist, and we find that bundling very large numbers of unrelated information goods can be surprisingly profitable. The reason is that the law of large numbers makes it much easier to predict consumers' valuations for a bundle of goods than their valuations for the individual goods when sold separately. As a result, this "predictive value of bundling" makes it possible to achieve greater sales, greater economic efficiency and greater profits per good from a bundle of information goods than can be attained when the same goods are sold separately. Our results do not extend to most physical goods, as the marginal costs of production typically negate any benefits from the predictive value of bundling. While determining optimal bundling strategies for more than two goods is a notoriously difficult problem, we use statistical techniques to provide strong asymptotic results and bounds on profits for bundles of any arbitrary size. We show how our model can be used to analyze the bundling of complements and substitutes, bundling in the presence of budget constraints and bundling of goods with various types of correlations. We find that when different market segments of consumers differ systematically in their valuations for goods, simple bundling will no longer be optimal. However, by offering a menu of different bundles aimed at each market segment, a monopolist can generally earn substantially higher profits than would be possible without bundling. The predictions of our analysis appear to be consistent with empirical observations of the markets for Internet and on-line content, cable television programming, and copyrighted music. ________________________________________ We thank Timothy Bresnahan, Hung-Ken Chien, Frank Fisher, Michael Harrison, Paul Kleindorfer, Thomas Malone, Robert Pindyck, Nancy Rose, Richard Schmalensee, John Tsitsiklis, Hal Varian, Albert Wenger, Birger Wernerfelt, four anonymous reviewers and seminar participants at the University of California at Berkeley, MIT, New York University, Stanford University, University of Rochester, the Wharton School, the 1995 Workshop on Information Systems and Economics and the 1998 Workshop on Marketing Science and the Internet for many helpful suggestions. Any errors that remain are only our responsibility.
An integrated fast battery charger for Electric Vehicle

Under the requirements of reducing emissions, air pollution and achieving higher fuel economy, companies are developing electric, hybrid electric, and plug-in hybrid electric vehicles. However, the high cost of these technologies and the low autonomy are very restrictive. In this paper a new concept of fast on-board battery charger for Electric Vehicles (EVs) is proposed which uses the electric motor like filter and the same converter for charging and traction mode.
CODOMs: Protecting software with Code-centric memory Domains

Today's complex software systems are neither secure nor reliable. The rudimentary software protection primitives provided by current hardware forces systems to run many distrusting software components (e.g., procedures, libraries, plugins, modules) in the same protection domain, or otherwise suffer degraded performance from address space switches.
 We present CODOMs (COde-centric memory DOMains), a novel architecture that can provide finer-grained isolation between software components with effectively zero run-time overhead, all at a fraction of the complexity of other approaches. An implementation of CODOMs in a cycle-accurate full-system x86 simulator demonstrates that with the right hardware support, finer-grained protection and run-time performance can peacefully coexist.
Fundamentals of Scatter Search and Path Relinking

The evolutionary approach called Scatter Search, and its generalized form called Path Relinking, have proved unusually effective for solving a diverse array of optimization problems from both classical and real world settings. Scatter Search and Path Relinking differ from other evolutionary procedures, such as genetic algorithms, by providing unifying principles for joining solutions based on generalized path constructions (in both Euclidean and neighborhood spaces) and by utilizing strategic designs where other approaches resort to randomization. Scatter Search and Path Relinking are also intimately related to the Tabu Search metaheuristic, and derive additional advantages by making use of adaptive memory and associated memory-exploiting mechanisms that are capable of being adapted to particular contexts. We describe the features of Scatter Search and Path Relinking that set them apart from other evolutionary approaches, and that offer opportunities for creating increasingly more versatile and effective methods in the future. † Partially supported by the visiting professor fellowship program of the University of Valencia (Grant Ref.
D Mesh Segmentation Methodologies for CAD applications

3D mesh segmentation is a fundamental process for Digital Shape Reconstruction in a variety of applications including Reverse Engineering, Medical Imaging, etc. It is used to provide a high level representation of the raw 3D data which is required for CAD, CAM and CAE. In this paper, we present an exhaustive overview of 3D mesh segmentation methodologies examining their suitability for CAD models. In particular, a classification of the various methods is given based on their corresponding underlying fundamental methodology concept as well as on the distinct criteria and features used in the segmentation process.
Indented Tree or Graph? A Usability Study of Ontology Visualization Techniques in the Context of Class Mapping Evaluation

Research effort in ontology visualization has largely focused on developing new visualization techniques. At the same time, researchers have paid less attention to investigating the usability of common visualization techniques that many practitioners regularly use to visualize ontological data. In this paper, we focus on two popular ontology visualization techniques: indented tree and graph. We conduct a controlled usability study with an emphasis on the effectiveness, efficiency, workload and satisfaction of these visualization techniques in the context of assisting users during evaluation of ontology mappings. Findings from this study have revealed both strengths and weaknesses of each visualization technique. In particular, while the indented tree visualization is more organized and familiar to novice users, subjects found the graph visualization to be more controllable and intuitive without visual redundancy, particularly for ontologies with multiple inheritance.
A broad-coverage collection of portable NLP components for building shareable analysis pipelines

Due to the diversity of natural language processing (NLP) tools and resources, combining them into processing pipelines is an important issue, and sharing these pipelines with others remains a problem. We present DKPro Core, a broad-coverage component collection integrating a wide range of third-party NLP tools and making them interoperable. Contrary to other recent endeavors that rely heavily on web services, our collection consists only of portable components distributed via a repository, making it particularly interesting with respect to sharing pipelines with other researchers, embedding NLP pipelines in applications, and the use on high-performance computing clusters. Our collection is augmented by a novel concept for automatically selecting and acquiring resources required by the components at runtime from a repository. Based on these contributions, we demonstrate a way to describe a pipeline such that all required software and resources can be automatically obtained, making it easy to share it with others, e.g. in order to reproduce results or as examples in teaching, documentation, or publications.
Focal-Test-Based Spatial Decision Tree Learning

Given learning samples from a raster data set, spatial decision tree learning aims to find a decision tree classifier that minimizes classification errors as well as salt-and-pepper noise. The problem has important societal applications such as land cover classification for natural resource management. However, the problem is challenging due to the fact that learning samples show spatial autocorrelation in class labels, instead of being independently identically distributed. Related work relies on local tests (i.e., testing feature information of a location) and cannot adequately model the spatial autocorrelation effect, resulting in salt-and-pepper noise. In contrast, we recently proposed a focal-test-based spatial decision tree (FTSDT), in which the tree traversal direction of a sample is based on both local and focal (neighborhood) information. Preliminary results showed that FTSDT reduces classification errors and salt-and-pepper noise. This paper extends our recent work by introducing a new focal test approach with adaptive neighborhoods that avoids over-smoothing in wedge-shaped areas. We also conduct computational refinement on the FTSDT training algorithm by reusing focal values across candidate thresholds. Theoretical analysis shows that the refined training algorithm is correct and more scalable. Experiment results on real world data sets show that new FTSDT with adaptive neighborhoods improves classification accuracy, and that our computational refinement significantly reduces training time.
Optoelectronically innervated soft prosthetic hand via stretchable optical waveguides

Because of their continuous and natural motion, fluidically powered soft actuators have shown potential in a range of robotic applications, including prosthetics and orthotics. Despite these advantages, robots using these actuators require stretchable sensors that can be embedded in their bodies for sophisticated functions. Presently, stretchable sensors usually rely on the electrical properties of materials and composites for measuring a signal; many of these sensors suffer from hysteresis, fabrication complexity, chemical safety and environmental instability, and material incompatibility with soft actuators. Many of these issues are solved if the optical properties of materials are used for signal transduction. We report the use of stretchable optical waveguides for strain sensing in a prosthetic hand. These optoelectronic strain sensors are easy to fabricate, are chemically inert, and demonstrate low hysteresis and high precision in their output signals. As a demonstration of their potential, the photonic strain sensors were used as curvature, elongation, and force sensors integrated into a fiber-reinforced soft prosthetic hand. The optoelectronically innervated prosthetic hand was used to conduct various active sensation experiments inspired by the capabilities of a real hand. Our final demonstration used the prosthesis to feel the shape and softness of three tomatoes and select the ripe one.
Computing Machinery and Intelligence Mind Vol. 59

In recent years, much research has been devoted to the synthesis of hash tables; contrarily, few have enabled the study of the partition table. Here, we confirm the evaluation of the Turing machine, which embodies the private principles of machine learning. Our focus in this position paper is not on whether thin clients can be made classical, adaptive, and concurrent, but rather on presenting a novel system for the evaluation of extreme programming (Plush). This is crucial to the success of our work.