nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive Web sites

D esigning a complex Web site so that it readily yields its information is a difficult task. The designer must anticipate the users' needs and structure the site accordingly. Yet users may have vastly differing views of the site's information, their needs may change over time, and their usage patterns may violate the designer's initial expectations. As a result, Web sites are all too often fossils cast in HTML, while user navigation is idiosyncratic and evolving. Understanding user needs requires understanding how users view the data available and how they actually use the site. For a complex site this can be difficult since user tests are expensive and time-consuming, and the site's server logs contain massive amounts of data. We propose a Web management assistant: a system that can process massive amounts of data about site usage Examining the potential use of automated adaptation to improve Web sites for visitors.
Information re-retrieval: repeat queries in Yahoo's logs

People often repeat Web searches, both to find new information on topics they have previously explored and to re-find information they have seen in the past. The query associated with a repeat search may differ from the initial query but can nonetheless lead to clicks on the same results. This paper explores repeat search behavior through the analysis of a one-year Web query log of 114 anonymous users and a separate controlled survey of an additional 119 volunteers. Our study demonstrates that as many as 40% of all queries are re-finding queries. Re-finding appears to be an important behavior for search engines to explicitly support, and we explore how this can be done. We demonstrate that changes to search engine results can hinder re-finding, and provide a way to automatically detect repeat searches and predict repeat clicks.
An Event-Based Neural Network Architecture With an Asynchronous Programmable Synaptic Memory

We present a hybrid analog/digital very large scale integration (VLSI) implementation of a spiking neural network with programmable synaptic weights. The synaptic weight values are stored in an asynchronous Static Random Access Memory (SRAM) module, which is interfaced to a fast current-mode event-driven DAC for producing synaptic currents with the appropriate amplitude values. These currents are further integrated by current-mode integrator synapses to produce biophysically realistic temporal dynamics. The synapse output currents are then integrated by compact and efficient integrate and fire silicon neuron circuits with spike-frequency adaptation and adjustable refractory period and spike-reset voltage settings. The fabricated chip comprises a total of 32 × 32 SRAM cells, 4 × 32 synapse circuits and 32 × 1 silicon neurons. It acts as a transceiver, receiving asynchronous events in input, performing neural computation with hybrid analog/digital circuits on the input spikes, and eventually producing digital asynchronous events in output. Input, output, and synaptic weight values are transmitted to/from the chip using a common communication protocol based on the Address Event Representation (AER). Using this representation it is possible to interface the device to a workstation or a micro-controller and explore the effect of different types of Spike-Timing Dependent Plasticity (STDP) learning algorithms for updating the synaptic weights values in the SRAM module. We present experimental results demonstrating the correct operation of all the circuits present on the chip.
A two-stage approach to blind spatially-varying motion deblurring

Many blind motion deblur methods model the motion blur as a spatially invariant convolution process. However, motion blur caused by the camera movement in 3D space during shutter time often leads to spatially varying blurring effect over the image. In this paper, we proposed an efficient two-stage approach to remove spatially-varying motion blurring from a single photo. There are three main components in our approach: (i) a minimization method of estimating region-wise blur kernels by using both image information and correlations among neighboring kernels, (ii) an interpolation scheme of constructing pixel-wise blur matrix from region-wise blur kernels, and (iii) a non-blind deblurring method robust to kernel errors. The experiments showed that the proposed method outperformed the existing software based approaches on tested real images.
Scheduling Aircraft Using Constraint Satisfaction

In this paper, an airport departure scheduling tool for aircraft is presented based on constraint satisfaction techniques. Airports are getting more and more congested with the available runway configuration as one of the most constraining factors. A possibility to alleviate this congestion is to assist controllers in the planning and scheduling process of aircraft. The prototype presented here is aimed to offer such assistance in the establishment of an optimal departure schedule and the planning of initial climb phases for departing aircraft. This goal is accomplished by modelling the scheduling problem as a constraint satisfaction problem, using ILOG Solver and Scheduler as an implementation environment.
Sortal anaphora resolution to enhance relation extraction from biomedical literature

Entity coreference is common in biomedical literature and it can affect text understanding systems that rely on accurate identification of named entities, such as relation extraction and automatic summarization. Coreference resolution is a foundational yet challenging natural language processing task which, if performed successfully, is likely to enhance such systems significantly. In this paper, we propose a semantically oriented, rule-based method to resolve sortal anaphora, a specific type of coreference that forms the majority of coreference instances in biomedical literature. The method addresses all entity types and relies on linguistic components of SemRep, a broad-coverage biomedical relation extraction system. It has been incorporated into SemRep, extending its core semantic interpretation capability from sentence level to discourse level. We evaluated our sortal anaphora resolution method in several ways. The first evaluation specifically focused on sortal anaphora relations. Our methodology achieved a F1 score of 59.6 on the test portion of a manually annotated corpus of 320 Medline abstracts, a 4-fold improvement over the baseline method. Investigating the impact of sortal anaphora resolution on relation extraction, we found that the overall effect was positive, with 50 % of the changes involving uninformative relations being replaced by more specific and informative ones, while 35 % of the changes had no effect, and only 15 % were negative. We estimate that anaphora resolution results in changes in about 1.5 % of approximately 82 million semantic relations extracted from the entire PubMed. Our results demonstrate that a heavily semantic approach to sortal anaphora resolution is largely effective for biomedical literature. Our evaluation and error analysis highlight some areas for further improvements, such as coordination processing and intra-sentential antecedent selection.
From Retinex to Automatic Color Equalization: issues in developing a new algorithm for unsupervised color equalization

nce ch; n ple iray r. In hue 003 Abstract. We present a comparison between two color equalization algorithms: Retinex, the famous model due to Land and McCann, and Automatic Color Equalization (ACE), a new algorithm recently presented by the authors. These two algorithms share a common approach to color equalization, but different computational models. We introduce the two models focusing on differences and common points. An analysis of their computational characteristics illustrates the way the Retinex approach has influenced ACE structure, and which aspects of the first algorithm have been modified in the second one and how. Their interesting equalization properties, like lightness and color constancy, image dynamic stretching, global and local filtering, and data driven dequantization, are qualitatively and quantitatively presented and compared, together with their ability to mimic the human visual system.
Deep Convolutional Neural Network Based Regression Approach for Estimation of Remaining Useful Life

Prognostics technique aims to accurately estimate the Remaining Useful Life (RUL) of a subsystem or a component using sensor data, which has many real world applications. However, many of the existing algorithms are based on linear models, which cannot capture the complex relationship between the sensor data and RUL. Although Multilayer Perceptron (MLP) has been applied to predict RUL, it cannot learn salient features automatically, because of its network structure. A novel deep Convolutional Neural Network (CNN) based regression approach for estimating the RUL is proposed in this paper. Although CNN has been applied on tasks such as computer vision, natural language processing, speech recognition etc., this is the first attempt to adopt CNN for RUL estimation in prognostics. Different from the existing CNN structure for computer vision, the convolution and pooling filters in our approach are applied along the temporal dimension over the multi-channel sensor data to incorporate automated feature learning from raw sensor signals in a systematic way. Through the deep architecture, the learned features are the higher-level abstract representation of low-level raw sensor signals. Furthermore, feature learning and RUL estimation are mutually enhanced by the supervised feedback. We compared with several state-of-the-art algorithms on two publicly available data sets to evaluate the effectiveness of this proposed approach. The encouraging results demonstrate that our proposed deep convolutional neural network based regression approach for RUL estimation is not only more efficient but also more accurate.
Posterior component separation with transversus abdominis release successfully addresses recurrent ventral hernias following anterior component separation

Anterior component separation (ACS) with external oblique release for ventral hernia repair has a recurrence rate up to 32 %. Hernia recurrence after prior ACS represents a complex surgical challenge. In this context, we report our experience utilizing posterior component separation with transversus abdominis muscle release (PCS/TAR) and retromuscular mesh reinforcement. Patients with a history of recurrent hernia following ACS repaired with PCS/TAR were retrospectively identified from prospective databases collected at two large academic institutions. Patient demographics, hernia characteristics (using CT scan) and outcomes were evaluated. Twenty-nine patients with a history of ACS developed 22 (76 %) midline, 3 (10 %) lateral and 4 (14 %) concomitant recurrences. Contamination was present in 11 (38 %) of cases. All were repaired utilizing a PCS/TAR with retromuscular mesh placement (83 % synthetic, 17 % biologic) and fascial closure. Wound morbidity consisted of 13 (45 %) surgical site occurrences including 8 (28 %) surgical site infections. Five (17 %) patients required 90-day readmission, and two (7 %) were related to wound morbidity. One organ space infection with frank spillage of stool resulted in the only instance of mesh excision. This case also represents the only instance of recurrence (3 %) with a mean follow-up of 11 (range 3–36) months. Patients with a history of an ACS who develop a recurrence represent a challenging clinical scenario with limited options for surgical repair. A PCS/TAR hernia repair achieves acceptable outcomes and may in fact be the best approach available.
Heuristics for Internet Map Discovery

Mercator is a program that uses hop-limited probes—the same primitive used in traceroute—to infer an Internet map. It uses informed random address probing to carefully exploring the IP address space when determining router adjacencies, uses source-route ca p ble routers wherever possible to enhance the fidelity of the resulting ma p, and employs novel mechanisms for resolvingaliases(interfaces belonging to the same router). This paper describes the design of these heuri stics and our experiences with Mercator, and presents some preliminary a nalysis of the resulting Internet map.
The X-ray object recognition test (X-ray ORT) - a reliable and valid instrument for measuring visual abilities needed in X-ray screening

Aviation security screening has become very important in recent years. It was shown by Schwaninger et al. (2004) that certain image-based factors influence detection when visually inspecting X-ray images of passenger bags. Threat items are more difficult to recognize when placed in close-packed bags (effect of bag complexity), when superimposed by other objects (effect of superposition), and when rotated (effect of viewpoint). The X-ray object recognition rest (X-ray ORT) was developed to measure the abilities needed to cope with these factors. In this study, we examined the reliability and validity of the X-ray ORT based on a sample of 453 aviation security screeners and 453 novices. Cronbach Alpha and split-half analysis revealed high reliability. Validity was examined using internal, convergent, discriminant and criterion-related validity estimates. The results show that the X-ray ORT is a reliable and valid instrument for measuring visual abilities needed in X-ray screening. This makes the X-ray ORT an interesting tool for competency and pre-employment assessment purposes.
Self-scheduled H∞ control of linear parameter-varying systems: a design example

This paper is concerned with the design of gain-scheduled controllers with guaranteed %$ performance for a class of linear parameter-varying (LPV) plants. Here the plant state-space matrices are assumed to depend affinely on a vector 8 of time-varying real parameters. Assuming real-time measurement of these parameters, they can be fed to the controller to optimize the performance and robustness of the closed-loop system. The resulting controller is time-varying and automatically ‘gain-scheduled’ along parameter trajectories. Based on the notion of quadratic %& performance, solvability conditions are obtained for continuousand discrete-time systems. In both cases the synthesis problem reduces to solving a system of linear matrix inequalities (LMIs). The main benefit of this approach is to bypass most difficulties associated with more classical schemes such as gain-interpolation or gain-scheduling techniques. The methodology presented in this paper is applied to the gain scheduling of a missile autopilot. The missile has a large operating range and high angles of attack. The dificulty of the problem is reinforced by tight performance requirements as well as the presence of flexible modes that limit the control bandwidth.
Cultural preferences for formal versus intuitive reasoning

The authors examined cultural preferences for formal versus intuitive reasoning among East Asian (Chinese and Korean), Asian American, and European American university students. We investigated categorization (Studies 1 and 2), conceptual structure (Study 3), and deductive reasoning (Studies 3 and 4). In each study a cognitive conflict was activated between formal and intuitive strategies of reasoning. European Americans, more than Chinese and Koreans, set aside intuition in favor of formal reasoning. Conversely, Chinese and Koreans relied on intuitive strategies more than European Americans. Asian Americans’ reasoning was either identical to that of European Americans, or intermediate. Differences emerged against a background of similar reasoning tendencies across cultures in the absence of conflict between formal and intuitive strategies.
Design of an 99%-efficient, 5kW, phase-shift PWM DC-DC converter for telecom applications

In the last decade power electronic research focused on the power density maximization mainly to reduce initial systems costs . In the field of data centers and telecom applications, the costs for powering and cooling exceed the purchasing cost in less than 2 years [2]. That causes the changing driving forces in the development of new power supplies to efficiency, while the power density should stay on a high level. The commonly used DC-DC converter in the power supply unit (PSU) for data centers and telecom applications are full bridge phase-shift converters since they meet the demands of high power and efficient power conversion, a compact design and the constant operation frequency allows a simple control and EMI design. The development of the converter with respect to high efficiency has a lot of degrees of freedom. An optimization procedure based on comprehensive analytical models leads to the optimal parameters (e.g. switching frequency, switching devices in parallel and transformer design) for the most efficient design. In this paper a 5kW, 400V–48⋖56V phase-shift PWM converter with LC-output filter is designed for highest efficiency (η ≥99%) with a volume limitation and the consideration of the part-load efficiency. The components dependency as well as the optimal design will be explained. The realized prototype design reaches a calculated efficiency of η = 99.2% under full load condition and a power density of ρ = 36W/in3 (2.2 kW/liter).
Broadband Self-Compensating Phase Shifter Combining Delay Line and Equal-Length Unequal-Width Phaser

In this paper, a broadband self-compensating phase shifter is presented and developed on the basis of substrate integrated waveguide (SIW) technology. Since the SIW is a dispersive guided-wave structure, the effective bandwidth of SIW phase shifter is usually narrow. Phase shifts generated by two different structures, namely delay line and equal-length unequal-width phase shifter, are discovered in this work to present interesting opposite tendencies versus frequency. Therefore, an appropriate combination of them will make the phase shift almost constant over a very wide band. Design equations and process are given following a mathematical analysis. To demonstrate the interesting and useful features of the proposed technique, a 90° and a 45° self-compensating phase shifters are designed as showcases with standard printed circuit board process. For the 90° version, the measured amplitude and phase imbalance between the two paths are within 0.2 dB and 2.5° , respectively, within the frequency band from 25.11 to 39.75 GHz, or around 49% bandwidth. The return loss is found to be better than 12 dB within the frequency band of interest. The 45° one has the similar excellent performance. The measurements demonstrate that this type of SIW phase shifter is superior to all of its counterparts.
Single lens 3D-camera with extended depth-of-field

Placing a micro lens array in front of an image sensor transforms a normal camera into a single lens 3D camera, which also allows the user to change the focus and the point of view after a picture has been taken. While the concept of such plenoptic cameras is known since 1908, only recently the increased computing power of low-cost hardware and the advances in micro lens array production, have made the application of plenoptic cameras feasible. This text presents a detailed analysis of plenoptic cameras as well as introducing a new type of plenoptic camera with an extended depth of field and a maximal effective resolution of up to a quarter of the sensor resolution.
Expletives: neurolinguistic and neurobehavioral perspectives on swearing

Severe aphasia, adult left hemispherectomy, Gilles de la Tourette syndrome (GTS), and other neurological disorders have in common an increased use of swearwords. There are shared linguistic features in common across these language behaviors, as well as important differences. We explore the nature of swearing in normal human communication, and then compare the clinical presentations of selectively preserved, impaired and augmented swearing. These neurolinguistic observations, considered along with related neuroanatomical and neurochemical information, provide the basis for considering the neurobiological foundation of various types of swearing behaviors.
Multi-agent systems and decentralized artificial superintelligence

Multi-agents systems communication is a technology, which provides a way for multiple interacting intelligent agents to communicate with each other and with environment. Multipleagent systems are used to solve problems that are difficult for solving by individual agent. Multiple-agent communication technologies can be used for management and organization of computing fog and act as a global, distributed operating system. In present publication we suggest technology, which combines decentralized P2P BOINC general-purpose computing tasks distribution, multiple-agents communication protocol and smart-contract based rewards, powered by Ethereum blockchain. Such system can be used as distributed P2P computing power market, protected from any central authority. Such decentralized market can further be updated to system, which learns the most efficient way for software-hardware combinations usage and optimization. Once system learns to optimize software-hardware efficiency it can be updated to general-purpose distributed intelligence, which acts as combination of single-purpose AI.
Combination of machine scores for automatic grading of pronunciation quality

This work is part of an eort aimed at developing computer-based systems for language instruction; we address the task of grading the pronunciation quality of the speech of a student of a foreign language. The automatic grading system uses SRI's Decipher continuous speech recognition system to generate phonetic segmentations. Based on these segmentations and probabilistic models we produce dierent pronunciation scores for individual or groups of sentences that can be used as predictors of the pronunciation quality. Dierent types of these machine scores can be combined to obtain a better prediction of the overall pronunciation quality. In this paper we review some of the bestperforming machine scores and discuss the application of several methods based on linear and nonlinear mapping and combination of individual machine scores to predict the pronunciation quality grade that a human expert would have given. We evaluate these methods in a database that consists of pronunciation-quality-graded speech from American students speaking French. With predictors based on spectral match and on durational characteristics, we ®nd that the combination of scores improved the prediction of the human grades and that nonlinear mapping and combination methods performed better than linear ones. Characteristics of the dierent nonlinear methods studied are discussed. Ó 2000 Elsevier Science B.V. All rights reserved.
Atlanta world: an expectation maximization framework for simultaneous low-level edge grouping and camera calibration in complex man-made environments

Edges in man-made environments, grouped according to vanishing point directions, provide single-view constraints that have been exploited before as a precursor to both scene understanding and camera calibration. A Bayesian approach to edge grouping was proposed in the "Manhattan World" paper by Coughlan and Yuille, where they assume the existence of three mutually orthogonal vanishing directions in the scene. We extend the thread of work spawned by Coughlan and Yuille in several significant ways. We propose to use the expectation maximization (EM) algorithm to perform the search over all continuous parameters that influence the location of the vanishing points in a scene. Because EM behaves well in high-dimensional spaces, our method can optimize over many more parameters than the exhaustive and stochastic algorithms used previously for this task. Among other things, this lets us optimize over multiple groups of orthogonal vanishing directions, each of which induces one additional degree of freedom. EM is also well suited to recursive estimation of the kind needed for image sequences and/or in mobile robotics. We present experimental results on images of "Atlanta worlds", complex urban scenes with multiple orthogonal edge-groups, that validate our approach. We also show results for continuous relative orientation estimation on a mobile robot.