nils-herrmann/clean_scirepeval huggingface dataset:
Anytime Exploitation of Stragglers in Synchronous Stochastic Gradient Descent

In this paper we propose an approach to parallelizing synchronous stochastic gradient descent (SGD) that we term “Anytime-Gradients”. The Anytime-Gradients is designed to exploit the work completed by slow compute nodes or “stragglers”. In many approaches work completed by these nodes, while only partial, is discarded completely. To maintain synchronization in our approach, each computational epoch is of fixed duration, and at the end of each epoch, workers send updated parameter vectors to a master mode for combination. The master weights each update by the amount of work done. The Anytime-Gradients scheme is robust to both persistent and non-persistent stragglers and requires no prior knowledge about processor abilities. We show that the scheme effectively exploits stragglers and outperforms existing methods.
The MIR flickr retrieval evaluation

In most well known image retrieval test sets, the imagery typically cannot be freely distributed or is not representative of a large community of users. In this paper we present a collection for the MIR community comprising 25000 images from the Flickr website which are redistributable for research purposes and represent a real community of users both in the image content and image tags. We have extracted the tags and EXIF image metadata, and also make all of these publicly available. In addition we discuss several challenges for benchmarking retrieval and classification methods.
Evolution of co-management: role of knowledge generation, bridging organizations and social learning.

Over a period of some 20 years, different aspects of co-management (the sharing of power and responsibility between the government and local resource users) have come to the forefront. The paper focuses on a selection of these: knowledge generation, bridging organizations, social learning, and the emergence of adaptive co-management. Co-management can be considered a knowledge partnership. Different levels of organization, from local to international, have comparative advantages in the generation and mobilization of knowledge acquired at different scales. Bridging organizations provide a forum for the interaction of these different kinds of knowledge, and the coordination of other tasks that enable co-operation: accessing resources, bringing together different actors, building trust, resolving conflict, and networking. Social learning is one of these tasks, essential both for the co-operation of partners and an outcome of the co-operation of partners. It occurs most efficiently through joint problem solving and reflection within learning networks. Through successive rounds of learning and problem solving, learning networks can incorporate new knowledge to deal with problems at increasingly larger scales, with the result that maturing co-management arrangements become adaptive co-management in time.
A Comprehensive Study Into Intrabody Communication Measurements

One of the main objectives of research into intrabody communication (IBC) is the characterization of the human body as a transmission medium for electrical signals. However, such characterization is strongly influenced by the conditions under which the experiments are performed. In addition, the outcomes reported in the literature vary according to the measurement method used, frequently making comparisons among them unfeasible. Further studies are still required in order to establish a methodology for IBC characterization and design. In this paper, both galvanic and capacitive coupling setups have been implemented and a comprehensive set of measurements has been carried out by analyzing fundamental IBC parameters such as optimum frequency range, maximum channel length, and type of electrodes, among others. Consequently, practical conclusions regarding the experimental conditions that optimize IBC performance for each coupling technique have been obtained.
A Kernel Between Sets of Vectors

In various application domains, including image recognition, it is natural to represent each example as a set of vectors. With a base kernel we can implicitly map these vectors to a Hilbert space and fit a Gaussian distribution to the whole set using Kernel PCA. We define our kernel between examples as Bhattacharyya’s measure of affinity between such Gaussians. The resulting kernel is computable in closed form and enjoys many favorable properties, including graceful behavior under transformations, potentially justifying the vector set representation even in cases when more conventional representations also exist.
Shape from Defocus via Diffusion

Defocus can be modeled as a diffusion process and represented mathematically using the heat equation, where image blur corresponds to the diffusion of heat. This analogy can be extended to nonplanar scenes by allowing a space-varying diffusion coefficient. The inverse problem of reconstructing 3D structure from blurred images corresponds to an "inverse diffusion" that is notoriously ill posed. We show how to bypass this problem by using the notion of relative blur. Given two images, within each neighborhood, the amount of diffusion necessary to transform the sharper image into the blurrier one depends on the depth of the scene. This can be used to devise a global algorithm to estimate the depth profile of the scene without recovering the deblurred image using only forward diffusion.
An efficient pattern mining approach for event detection in multivariate temporal data

This work proposes a pattern mining approach to learn event detection models from complex multivariate temporal data, such as electronic health records. We present recent temporal pattern mining, a novel approach for efficiently finding predictive patterns for event detection problems. This approach first converts the time series data into time-interval sequences of temporal abstractions. It then constructs more complex time-interval patterns backward in time using temporal operators. We also present the minimal predictive recent temporal patterns framework for selecting a small set of predictive and non-spurious patterns. We apply our methods for predicting adverse medical events in real-world clinical data. The results demonstrate the benefits of our methods in learning accurate event detection models, which is a key step for developing intelligent patient monitoring and decision support systems.
Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation

1077-3142/$ see front matter 2010 Elsevier Inc. A doi:10.1016/j.cviu.2010.09.007 ⇑ Corresponding author.  E-mail address: su.ruan@univ-rouen.fr (S. Ruan). This paper presents a framework of a medical image analysis system for the brain tumor segmentation and the brain tumor following-up over time using multi-spectral MRI images. Brain tumors have a large diversity in shape and appearance with intensities. Multi-spectral images have the advantage in providing complementary information to resolve some ambiguities. However, they may also bring along a lot of redundant information, increasing the data processing time and segmentation errors. The challenge is how to make use of the multi-spectral images effectively. Our idea of fusing these data is to extract the most useful features to obtain the best segmentation with the least cost in time. The Support Vector Machine (SVM) classification integrated with a selection of the features in a kernel space is proposed. The selection criteria are defined by the kernel class separability. Based on this SVM classification a framework to follow up the brain tumor evolution is proposed, which consists of the following steps: (1) to learn the brain tumor and select the features from the first MRI examination of the patients; (2) to automatically segment the tumor in new data using SVM; (3) to refine the tumor contour by a region growing technique. The system has been tested on real patient images with satisfying results. The quantitative evaluations by comparing with experts’ manual traces and with other approaches demonstrate the effectiveness of the proposed method.
Sliding-Mode Velocity Control of Mobile-Wheeled Inverted-Pendulum Systems

There has been increasing interest in a type of underactuated mechanical systems, mobile-wheeled inverted-pendulum (MWIP) models, which are widely used in the field of autonomous robotics and intelligent vehicles. Robust-velocity-tracking problem of the MWIP systems is investigated in this study. In the velocity-control problem, model uncertainties accompany uncertain equilibriums, which make the controller design become more difficult. Two sliding-mode-control (SMC) methods are proposed for the systems, both of which are capable of handling both parameter uncertainties and external disturbances. The asymptotical stabilities of the corresponding closed-loop systems are achieved through the selection of sliding-surface parameters, which are based on some rules. There is still a steady tracking error when the first SMC controller is used. By assuming a novel sliding surface, the second SMC controller is designed to solve this problem. The effectiveness of the proposed methods is finally confirmed by the numerical simulations.
Simulation and analysis of distributed PV generation in a LV network using MATLAB-Simulink

Power quality of electric networks with high penetration of photovoltaic systems has been analyzed in different publications. It has been noted that inverters may increase their harmonic emissions when connected to weak networks, this harmonics may affect other equipments connected to the network and decreasing their performance. This document shows the effects of connecting multiple photovoltaic generators in a network through simulations using MATLAB-SimulinkSimPowerSystems.
Linked Data: Evolving the Web into a Global Data Space

The World Wide Web has enabled the creation of a global information space comprising linked documents. As the Web becomes ever more enmeshed with our daily lives, there is a growing desire for direct access to raw data not currently available on the Web or bound up in hypertext documents. Linked Data provides a publishing paradigm in which not only documents, but also data, can be a first class citizen of the Web, thereby enabling the extension of the Web with a global data space based on open standards the Web of Data. In this Synthesis lecture we provide readers with a detailed technical introduction to Linked Data. We begin by outlining the basic principles of Linked Data, including coverage of relevant aspects of Web architecture. The remainder of the text is based around two main themes the publication and consumption of Linked Data. Drawing on a practical Linked Data scenario, we provide guidance and best practices on: architectural approaches to publishing Linked Data; choosing URIs and vocabularies to identify and describe resources; deciding what data to return in a About the Book The book is published by Morgan & Claypool in the series Synthesis Lectures on the Semantic Web: Theory and Technology edited by James Hendler and Frank van Harmelen. Please cite the book as: Tom Heath and Christian Bizer (2011) Linked Data: Evolving the Web into a Global Data Space (1st edition). Synthesis Lectures on the Semantic Web: Theory and Technology, 1:1, 1-136. Morgan & Claypool.  9781608454303 (paperback) ISBN: 9781608454310 (ebook)  If you are a member of an organization that has licensed the Morgan & Claypool Synthesis Lectures collection, you can download the book from Morgan & Claypool. ¡ If you are not a member of an organization that has licensed the Morgan & Claypool Synthesis Lectures collection, you can buy unlimited electronic access from Morgan & Claypool for 30 US$. l Hard Copy ¡ Available from Morgan & Claypool for 35 US$.  Evolving the Web into a Global Data Space 2011-04-06 http://linkeddatabook.com/editions/1.0/ description of a resource on the Web; methods and frameworks for automated linking of data sets; and testing and debugging approaches for Linked Data deployments. We give an overview of existing Linked Data applications and then examine the architectures that are used to consume Linked Data from the Web, alongside existing tools and frameworks that enable these. Readers can expect to gain a rich technical understanding of Linked Data fundamentals, as the basis for application development, research or further study.
A wearable heart rate belt for ambulant ECG monitoring

Long-term ECG monitoring is desirable in many daily healthcare situations where a wearable device that can continuously record ECG signals is needed. In this work, we proposed a wearable heart rate belt for ambulant ECG monitoring which can be comfortably worn on the chest or the waist. Active textile electrodes were designed for ECG recording. And a battery powered circuit board was developed consisting of ECG signal conditioning circuits, a 3-axis accelerator for body motion detection, a 12-bit AD converter, a DSP for signal processing and a SD card for data storage. The system also includes a wireless communication module that can transmit heart rate data to a sport watch for displaying. Experiments were carried out which shows that the proposed system is unobtrusive and can be comfortably worn by the user during daily activities. When wearing on the waist, ECG signals with reasonably good quality were acquired in rest and walking situations. The proposed system shows promise for long-term ambulant ECG monitoring.
Improving public transit accessibility for blind riders by crowdsourcing bus stop landmark locations with Google street view

Low-vision and blind bus riders often rely on known physical landmarks to help locate and verify bus stop locations (e.g., by searching for a shelter, bench, newspaper bin). However, there are currently few, if any, methods to determine this information a priori via computational tools or services. In this paper, we introduce and evaluate a new scalable method for collecting bus stop location and landmark descriptions by combining online crowdsourcing and Google Street View (GSV). We conduct and report on three studies in particular: (i) a formative interview study of 18 people with visual impairments to inform the design of our crowdsourcing tool; (ii) a comparative study examining differences between physical bus stop audit data and audits conducted virtually with GSV; and (iii) an online study of 153 crowd workers on Amazon Mechanical Turk to examine the feasibility of crowdsourcing bus stop audits using our custom tool with GSV. Our findings reemphasize the importance of landmarks in non-visual navigation, demonstrate that GSV is a viable bus stop audit dataset, and show that minimally trained crowd workers can find and identify bus stop landmarks with 82.5% accuracy across 150 bus stop locations (87.3% with simple quality control).
Augmented Reality through Wearable Computing

Wearable computing moves computation from the desktop to the user. We are forming a community of networked, wearable-computer users to explore, over a long period, the augmented realities that these systems can provide. By adapting its behavior to the user's changing environment, a body-worn computer can assist the user more intelligently, consistently, and continuously than a desktop system. A text-based augmented reality, the Remembrance Agent, is presented to illustrate this approach. Video cameras are used both to warp the visual input (mediated reality) and to sense the user's world for graphical overlay. With a camera, the computer could track the user's finger to act as the system's mouse; perform face recognition; and detect passive objects to overlay 2.5D and 3D graphics onto the real world. Additional apparatus such as audio systems, infrared beacons for sensing location, and biosensors for learning about the wearer's affect are described. With the use of input from these interface devices and sensors, a long-term goal of this project is to model the user's actions, anticipate his or her needs, and perform a seamless interaction between the virtual and physical environments.
COURSE ASSESSMENT PRACTICES AND STUDENT LEARNING STRATEGIES IN ONLINE COURSES

Perhaps the most promising and understudied aspect of online education is course assessment. Course assessment is important because it has a strong impact on learning and is an indicator of the quality of learning occurring in a class. In the online environment, methods of assessment can be very different. However, the online education literature is currently lacking empirical data about the general status of assessment practices or how those practices relate to student learning. This article lays the groundwork for future studies by providing a description of formative and summative assessment and learning strategies in 60 online courses and suggesting some ways that assessment practices lead to different types of learning. In this study, instructors appear to follow effective practice by using multiple and alternative assessment methods, dispersing grades over time, and providing timely and frequent feedback to students. Students report focusing on relatively more complex learning strategies, such as elaboration and critical thinking over rehearsal. However, online instructors need to ensure that assessments are used strategically and that feedback is productive and able to be acted upon by students.
Using Stories to Teach Human Values to Artificial Agents

Value alignment is a property of an intelligent agent indicating that it can only pursue goals that are beneficial to humans. Successful value alignment should ensure that an artificial general intelligence cannot intentionally or unintentionally perform behaviors that adversely affect humans. This is problematic in practice since it is difficult to exhaustively enumerated by human programmers. In order for successful value alignment, we argue that values should be learned. In this paper, we hypothesize that an artificial intelligence that can read and understand stories can learn the values tacitly held by the culture from which the stories originate. We describe preliminary work on using stories to generate a value-aligned reward signal for reinforcement learning agents that prevents psychotic-appearing behavior.
Metaphors We Live By

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A case study of using HCI methods to improve tools for programmers

For more than five years, researchers at Carnegie Mellon University have been collaborating with several SAP teams to improve the usability of SAP's developer tools and programming APIs. Much research has shown that HCI techniques can improve the tools that developers use to write software. In a recent project, we applied HCI techniques to a SAP developer tool for the SAP NetWeaver Gateway product. The SAP team building this tool uses agile software development processes, which allowed them to quickly improve the tool's usability based upon the evaluations.
Application of Requirement-oriented Data Quality Evaluation Method

The data quality directly determines the data further analysis and other applications. With the important of data quality, data is a major asset in software applications and information systems. Getting and ensuring high quality data is critical to the software and related business operations. At present, there are many methods and techniques of data quality assessment or evaluation. Based on the evaluation criteria of data quality, the paper mainly starts from the needs of application software and uses the requirements as a guide in the evaluation process. Define evaluation criteria based on user-defined requirements. In this paper, the method proposed in a practical case has been applied, and achieved good practical results.
An Inoperability Input-Output Model (IIM) for disruption propagation analysis

Today's Supply Chains (SCs) are global and getting more complex, due to the exigencies of the world economy, making them hard to manage. This is exacerbated by the increasingly frequent occurrence of natural disasters and other high impact disruptions. To stay competitive, companies are therefore seeking ways to better understand the impact of such disruptions to their SCs. In addressing this need, this paper proposes an approach for disruption propagation analysis. We develop a method based on a variation of an Inoperability Input-Output Model (adapted from a Leontief I-O model) to quantify the impact of disruptions across the entire SC. We then analyse the factors that have the most influential impacts on the SCs during disruptions. Initial results show that trading volume handled by a company/ node is an important factor in determining the disruption impact to SCs, besides the number of SC partners (connections) as implied from previous work.
Opportunities and Challenges of Switched Reluctance Motor Drives for Electric Propulsion: A Comparative Study

Selection of the proper electric traction drive is an important step in design and performance optimization of electrified powertrains. Due to the use of high energy magnets, permanent magnet synchronous machines (PMSM) have been the primary choice in the electric traction motor market. However, manufacturers are very interested to find a permanent magnet-free alternative as a fallback option due to unstable cost of rare-earth metals and fault tolerance issues related to the constant permanent magnet excitation. In this paper, a new comprehensive review of electric machines (EMs) that includes various new switched reluctance machine topologies in addition to conventional EMs such as PMSM, induction machine, synchronous reluctance machine (SynRel), and PM-assisted SynRel is presented. This paper is based on performances such as power density, efficiency, torque ripple, vibration and noise, and fault tolerance. These systematic examinations prove that recently proposed magnetic configurations such as double-stator switched reluctance machine can be a reasonable substitute for permanent magnet machines in electric traction applications.
Reinforcement Learning for Visual Object Detection

One of the most widely used strategies for visual object detection is based on exhaustive spatial hypothesis search. While methods like sliding windows have been successful and effective for many years, they are still brute-force, independent of the image content and the visual category being searched. In this paper we present principled sequential models that accumulate evidence collected at a small set of image locations in order to detect visual objects effectively. By formulating sequential search as reinforcement learning of the search policy (including the stopping condition), our fully trainable model can explicitly balance for each class, specifically, the conflicting goals of exploration - sampling more image regions for better accuracy -, and exploitation - stopping the search efficiently when sufficiently confident about the target's location. The methodology is general and applicable to any detector response function. We report encouraging results in the PASCAL VOC 2012 object detection test set showing that the proposed methodology achieves almost two orders of magnitude speed-up over sliding window methods.