nils-herrmann/clean_scirepeval huggingface dataset:
General purpose computer-assisted clustering and conceptualization.

We develop a computer-assisted method for the discovery of insightful conceptualizations, in the form of clusterings (i.e., partitions) of input objects. Each of the numerous fully automated methods of cluster analysis proposed in statistics, computer science, and biology optimize a different objective function. Almost all are well defined, but how to determine before the fact which one, if any, will partition a given set of objects in an "insightful" or "useful" way for a given user is unknown and difficult, if not logically impossible. We develop a metric space of partitions from all existing cluster analysis methods applied to a given dataset (along with millions of other solutions we add based on combinations of existing clusterings) and enable a user to explore and interact with it and quickly reveal or prompt useful or insightful conceptualizations. In addition, although it is uncommon to do so in unsupervised learning problems, we offer and implement evaluation designs that make our computer-assisted approach vulnerable to being proven suboptimal in specific data types. We demonstrate that our approach facilitates more efficient and insightful discovery of useful information than expert human coders or many existing fully automated methods.
A Minimum Delay Routing Algorithm Using Distributed Computation

73 generalized vertex median of a weighted graph, "  and 1973, respectively. He spent two and a half years at Bell Laboratories , Murray Hill, NJ, developing telemetrized automatic surveillance and control systems. He is now Manager at Data Communications Systems, Vienna, VA, where he has major responsibilities in research and development of network analysis and design capabilities, and has applied these capabilities in the direction of projects ranging from feasability analysis and design of front end processors for the Navy to development of network architectures for the FAA. NY, responsible for contributing to the ongoing research in the areas of large network design, topological optimization for terminal access, the concentrator location problem, and flow and congestion control strategies for packet switching networks. At present, Absfruct-An algorithm is defined for establishing routing tables in the individual nodes of a data network. The routing fable at a node i specifies, for each other node j , what fraction of the traffic destined far node j should leave node i on each of the links emanating from node i. The algorithm is applied independently at each node and successively updates the routing table at that node based on information communicated between adjacent nodes about the marginal delay to each destination. For stationary input traffic statistics, the average delay per message through the network converges, with successive updates of the routing tables, to the minimum average delay over all routing assignments. The algorithm has the additional property that the traffic to each destination is guaranteed to be loop free at each iteration of the algorithm. In addition, a new global convergence theorem for non-continuous iteration algorithms is developed.  T HE problem of routing assignments has been one of the most intensively studied areas in the field of data networks in recent years. These routing problems can be roughly classified as static routing, quasi-static routing, and dynamic routing. Static routing can be typified by the following type of problem. One wishes to establish a new data network and makes various assumptions about the node locations, the link locations, and the capacities of the links. Given the traffic between each source and destination, one can calculate the traffic on each link as a function of the routing of the traffic. If one approximates the queueing delays on each link as a function of the link traffic, one can …
elastix: A Toolbox for Intensity-Based Medical Image Registration

Medical image registration is an important task in medical image processing. It refers to the process of aligning data sets, possibly from different modalities (e.g., magnetic resonance and computed tomography), different time points (e.g., follow-up scans), and/or different subjects (in case of population studies). A large number of methods for image registration are described in the literature. Unfortunately, there is not one method that works for all applications. We have therefore developed elastix, a publicly available computer program for intensity-based medical image registration. The software consists of a collection of algorithms that are commonly used to solve medical image registration problems. The modular design of elastix allows the user to quickly configure, test, and compare different registration methods for a specific application. The command-line interface enables automated processing of large numbers of data sets, by means of scripting. The usage of elastix for comparing different registration methods is illustrated with three example experiments, in which individual components of the registration method are varied.
Automatically Generating Rhythmic Verse with Neural Networks

We propose two novel methodologies for the automatic generation of rhythmic poetry in a variety of forms. The first approach uses a neural language model trained on a phonetic encoding to learn an implicit representation of both the form and content of English poetry. This model can effectively learn common poetic devices such as rhyme, rhythm and alliteration. The second approach considers poetry generation as a constraint satisfaction problem where a generative neural language model is tasked with learning a representation of content, and a discriminative weighted finite state machine constrains it on the basis of form. By manipulating the constraints of the latter model, we can generate coherent poetry with arbitrary forms and themes. A large-scale extrinsic evaluation demonstrated that participants consider machine-generated poems to be written by humans 54% of the time. In addition, participants rated a machinegenerated poem to be the most human-like amongst all evaluated.
Fast time intervals mining using the transitivity of temporal relations

We introduce an algorithm, called KarmaLego, for the discovery of frequent symbolic time interval-related patterns (TIRPs). The mined symbolic time intervals can be part of the input, or can be generated by a temporal-abstraction process from raw time-stamped data. The algorithm includes a data structure for TIRP-candidate generation and a novel method for efficient candidate-TIRP generation, by exploiting the transitivity property of Allen’s temporal relations. Additionally, since the non-ambiguous definition of TIRPs does not specify the duration of the time intervals, we propose to pre-cluster the time intervals based on their duration to decrease the variance of the supporting instances. Our experimental comparison of the KarmaLego algorithm’s runtime performance with several existing state of the art time intervals pattern mining methods demonstrated a significant speed-up, especially with large datasets and low levels of minimal vertical support. Furthermore, pre-clustering by time interval duration led to an increase in the homogeneity of the duration of the discovered TIRP’s supporting instances’ time intervals components, accompanied, however, by a corresponding decrease in the number of discovered TIRPs.
A simplification of the protein assay method of Lowry et al. which is more generally applicable.

Some recent modifications of the protein assay by the method of Lowry, Rosebrough, Farr, and Randall (1951, .I. Biol. Chem. 193, 265-275) have been reexamined and altered to provide a consolidated method which is simple, rapid, objective, and more generally applicable. A DOC-TCA protein precipitation technique provides for rapid quantitative recovery of soluble and membrane proteins from interfering substances even in very dilute solutions (< 1 pg/ml of protein). SDS is added to alleviate possible nonionic and cationic detergent and lipid interferences, and to provide mild conditions for rapid denaturation of membrane and proteolipid proteins. A simple method based on a linear log-log protein standard curve is presented to permit rapid and totally objective protein analysis using small programmable calculators. The new modification compared favorably with the original method of Lowry ef al.
A novel highly efficient broadband continuous class-F RFPA delivering 74% average efficiency for an octave bandwidth

A novel, highly efficient and broadband RF power amplifier (PA) operating in “continuous class-F” mode has been realized for first time. The introduction and experimental verification of this new PA mode demonstrates that it is possible to maintain expected output performance, both in terms of efficiency and power, over a very wide bandwidth. Using recently established continuous class-F theory, an output matching network was designed to terminate the first three harmonic impedances. This resulted in a PA delivering an average drain efficiency of 74% and average output power of 10.5W for an octave bandwidth between 0.55GHz and 1.1GHz. A commercially available 10W GaN HEMT transistor has been used for the PA design and realization.
Quantitative Comparison of Flux-Switching Permanent-Magnet Motors With Interior Permanent Magnet Motor for EV, HEV, and PHEV Applications

Permanent-magnet (PM) motors with both magnets and armature windings on the stator (stator PM motors) have attracted considerable attention due to their simple structure, robust configuration, high power density, easy heat dissipation, and suitability for high-speed operations. However, current PM motors in industrial, residential, and automotive applications are still dominated by interior permanent-magnet motors (IPM) because the claimed advantages of stator PM motors have not been fully investigated and validated. Hence, this paper will perform a comparative study between a stator-PM motor, namely, a flux switching PM motor (FSPM), and an IPM which has been used in the 2004 Prius hybrid electric vehicle (HEV). For a fair comparison, the two motors are designed at the same phase current, current density, and dimensions including the stator outer diameter and stack length. First, the Prius-IPM is investigated by means of finite-element method (FEM). The FEM results are then verified by experimental results to confirm the validity of the methods used in this study. Second, the FSPM design is optimized and investigated based on the same method used for the Prius-IPM. Third, the electromagnetic performance and the material mass of the two motors are compared. It is concluded that FSPM has more sinusoidal back-EMF hence is more suitable for BLAC control. It also offers the advantage of smaller torque ripple and better mechanical integrity for safer and smoother operations. But the FSPM has disadvantages such as low magnet utilization ratio and high cost. It may not be able to compete with IPM in automotive and other applications where cost constraints are tight.
A Robust 3D-2D Interactive Tool for Scene Segmentation and Annotation

Recent advances of 3D acquisition devices have enabled large-scale acquisition of 3D scene data. Such data, if completely and well annotated, can serve as useful ingredients for a wide spectrum of computer vision and graphics works such as data-driven modeling and scene understanding, object detection and recognition. However, annotating a vast amount of 3D scene data remains challenging due to the lack of an effective tool and/or the complexity of 3D scenes (e.g., clutter, varying illumination conditions). This paper aims to build a robust annotation tool that effectively and conveniently enables the segmentation and annotation of massive 3D data. Our tool works by coupling 2D and 3D information via an interactive framework, through which users can provide high-level semantic annotation for objects. We have experimented our tool and found that a typical indoor scene could be well segmented and annotated in less than 30 minutes by using the tool, as opposed to a few hours if done manually. Along with the tool, we created a dataset of over a hundred 3D scenes associated with complete annotations using our tool. Both the tool and dataset are available at http://scenenn.net.
Design and operational control of an AGV system

In this paper we first deal with the design and operational control of Automated Guided Vehicle (AGV) systems, starting from the literature on these topics. Three main issues emerge: track layout, the number of AGVs required and operational transportation control. An hierarchical queueing network approach to determine the number of AGVs is decribed. Also basic concepts are presented for the transportation control of both a job-shop and a flow-shop. Next we report on the results of a case study, in which track layout and transportation control are the main issues. Finally we suggest some topics for further research.
Big Data solutions in Healthcare: Problems and perspectives

Data in the healthcare sector is growing beyond dealing capacity of the health care organizations and is expected to increase significantly in the coming years. Majority of the Healthcare data is often unstructured, exists in silos and resides in imaging systems, medical prescription notes, insurance claims data, EPR (Electronic Patient Records) etc. integrating these heterogeneous data and factoring it in to advance analytics is critical to improve healthcare outcomes. Either because data are isolated in disparate or incompatible formats or due to the lack in processing capability to load and query large datasets in a timely fashion the Healthcare organizations are not in a position to leverage the benefits of the vast data they have. With convergence of advanced computing and numerous Big Data technological options like commercial solutions, Open Source, Cloud etc. it is now possible to attain high performance, scalability at a relatively low cost. Big data solutions often come with set of innovative data management solutions and analytical tools, when effectively implemented can transform the healthcare outcomes.
The Use of Social Media in the Supply Chain: Survey and Extensions

This paper investigates capabilities of social media, such as Facebook, Twitter, Delicious, Digg and others, for their current and potential impact on the supply chain. In particular, this paper examines the use of social media to capture the impact on supply chain events and develop a context for those events. This paper also analyzes the use of social media in the supply chain to build relationships among supply chain participants. Further, this paper investigates the of use user supplied tags as a basis of evaluating and extending an ontology for supply chains. In addition, using knowledge discovery from social media, a number of concepts related to the supply chain are examined, including supply chain reputation and influence within the supply chain. Prediction markets are analyzed for their potential use in supply chains. Finally, this paper investigates the integration of traditional knowledge management along with knowledge generated from social media.
Social cognitive theory of posttraumatic recovery: the role of perceived self-efficacy.

The present article integrates findings from diverse studies on the generalized role of perceived coping self-efficacy in recovery from different types of traumatic experiences. They include natural disasters, technological catastrophes, terrorist attacks, military combat, and sexual and criminal assaults. The various studies apply multiple controls for diverse sets of potential contributors to posttraumatic recovery. In these different multivariate analyses, perceived coping self-efficacy emerges as a focal mediator of posttraumatic recovery. Verification of its independent contribution to posttraumatic recovery across a wide range of traumas lends support to the centrality of the enabling and protective function of belief in one's capability to exercise some measure of control over traumatic adversity.
So fast so good: An analysis of answer quality and answer speed in community Question-answering sites

The authors investigate the interplay between answer quality and answer speed across question types in community question-answering sites (CQAs). The research questions addressed are the following: (a) How do answer quality and answer speed vary across question types? (b) How do the relationships between answer quality and answer speed vary across question types? (c) How do the best quality answers and the fastest answers differ in terms of answer quality and answer speed across question types? (d) How do trends in answer quality vary over time across question types? From the posting of 3,000 questions in six CQAs, 5,356 answers were harvested and analyzed. There was a significant difference in answer quality and answer speed across question types, and there were generally no significant relationships between answer quality and answer speed. The best quality answers had better overall answer quality than the fastest answers but generally took longer to arrive. In addition, although the trend in answer quality had been mostly random across all question types, the quality of answers appeared to improve gradually when given time. By highlighting the subtle nuances in answer quality and answer speed across question types, this study is an attempt to explore a territory of CQA research that has hitherto been relatively uncharted.
Fast Localization and Segmentation of Optic Disk in Retinal Images Using Directional Matched Filtering and Level Sets

The optic disk (OD) center and margin are typically requisite landmarks in establishing a frame of reference for classifying retinal and optic nerve pathology. Reliable and efficient OD localization and segmentation are important tasks in automatic eye disease screening. This paper presents a new, fast, and fully automatic OD localization and segmentation algorithm developed for retinal disease screening. First, OD location candidates are identified using template matching. The template is designed to adapt to different image resolutions. Then, vessel characteristics (patterns) on the OD are used to determine OD location. Initialized by the detected OD center and estimated OD radius, a fast, hybrid level-set model, which combines region and local gradient information, is applied to the segmentation of the disk boundary. Morphological filtering is used to remove blood vessels and bright regions other than the OD that affect segmentation in the peripapillary region. Optimization of the model parameters and their effect on the model performance are considered. Evaluation was based on 1200 images from the publicly available MESSIDOR database. The OD location methodology succeeded in 1189 out of 1200 images (99% success). The average mean absolute distance between the segmented boundary and the reference standard is 10% of the estimated OD radius for all image sizes. Its efficiency, robustness, and accuracy make the OD localization and segmentation scheme described herein suitable for automatic retinal disease screening in a variety of clinical settings.
Radio Resource Allocation Techniques for Efficient Spectrum Access in Cognitive Radio Networks

This paper provides an overview of cognitive radio (CR) networks, with focus on the recent advances in resource allocation techniques and the CR networks architectural design. The contribution of this work is threefold. First, a systematic way to study the resource allocation problem is presented; various design approaches are introduced, such as signal-to-interference-and-noise ratio (SINR) or transmission power-based, and centralized or distributed methods. Second, CR optimization methods are presented, accompanied by a comprehensive study of the resource allocation problem formulations. Furthermore, quality of service criteria of the physical or/and the medium access control layers are investigated. Third, challenges in spectrum assignment are discussed, focusing on dynamic spectrum allocation, spectrum aggregation and frequency mobility. Such approaches constitute an emerging trend in efficient spectrum sharing and affect the performance of resource allocation techniques. The open issues for future research in this area are finally discussed, including adaptability-reconfigurability, dual accessibility, and energy efficiency.
A 1V supply 50nV//spl radic/Hz noise PSD CMOS amplifier using noise reduction technique of autozeroing and chopper stabilization

A low-noise CMOS amplifier operating at a low supply voltage is developed using the two noise reduction techniques of autozeroing and chopper stabilization. The proposed amplifier utilizes a feedback with virtual grounded input-switches and a multiple-output switched op-amp. The low-noise amplifier fabricated in a 0.18-/spl mu/m CMOS technology achieved 50-nV/VHz input noise at 1-MHz chopping and 0.5-mW power consumption at 1-V supply voltage.
A 2D Barcode-Based Mobile Payment System

Mobile payment is very important and critical solution for mobile commerce. A user-friendly mobile payment solution is strongly needed to support mobile users to conduct secure and reliable payment transactions using mobile devices. This paper presents an innovative mobile payment system based on 2-Dimentional (2D) barcodes for mobile users to improve mobile user experience in mobile payment.Unlike other existing mobile payment systems, the proposed payment solution provides distinct advantages to support buy-and-sale products and services based on 2D Barcodes.This system uses one standard 2D Barcode (DataMatrix) as an example to demonstrate how to deal with underlying mobile business workflow, mobile transactions and security issues. The paper discusses system architecture, design and implementation of the proposed mobile payment solution, as well as 2D barcode based security solutions. In addition, this paper also presents some application examples of the system.
A Generative Programming Approach for Game Development

Nowadays, due to the great distance between design and implementation worlds, different skills are necessary to create a game system. To solve this problem, a lot of strategies for game development, trying to increase the abstraction level necessary for the game production, were proposed. In this way, a lot of game engines, game frameworks and others, in most cases without any compatibility or reuse criteria between them, were developed. This paper presents a new generative programming approach, able to increase the production of a digital game by the integration of different game development artifacts, following a system family strategy focused on variable and common aspects of a computer game. As result, high level abstractions of games, based on a common language, can be used to configure met programming transformations during the game production, providing a great compatibility level between game domain and game implementation artifacts.