nils-herrmann/clean_scirepeval huggingface dataset:
Visualizing Linked Data with JavaScript

Despite the wealth of information contained in the Web of Linked Data, the current limitations and entry barriers of the Semantic Web technologies hinder the users from taking advantage of these information resources. Linked Data visualization can alleviate this problem. In this paper, we adopt a proper Linked Data visualization model, design Linked Data visualization algorithms, and develop a lightweight, easy-to-use prototype tool, LOD Viewer, using the platform independent JavaScript language. LOD Viewer can visualize different sources of RDF data including SPARQL endpoints for Linked Open Data (LOD) sources, and display the data in different graphic illustrations. Our case studies have verified the effectiveness and realizability of the proposed method. The time complexity analysis and experimental test show that the run-time of the proposed algorithms approximately exhibits a linear growth rate as the visualized RDF triples size increases.
Effective inter-component communication mapping in Android with Epicc: an essential step towards holistic security analysis

Many threats present in smartphones are the result of interactions between application components, not just artifacts of single components. However, current techniques for identifying inter-application communication are ad hoc and do not scale to large numbers of applications. In this paper, we reduce the discovery of inter-component communication (ICC) in smartphones to an instance of the Interprocedural Distributive Environment (IDE) problem, and develop a sound static analysis technique targeted to the Android platform. We apply this analysis to 1,200 applications selected from the Play store and characterize the locations and substance of their ICC. Experiments show that full specifications for ICC can be identified for over 93% of ICC locations for the applications studied. Further the analysis scales well; analysis of each application took on average 113 seconds to complete. Epicc, the resulting tool, finds ICC vulnerabilities with far fewer false positives than the next best tool. In this way, we develop a scalable vehicle to extend current security analysis to entire collections of applications as well as the interfaces they export.
One shot learning of simple visual concepts

People can learn visual concepts from just one encounter, but it remains a mystery how this is accomplished. Many authors have proposed that transferred knowledge from more familiar concepts is a route to one shot learning, but what is the form of this abstract knowledge? One hypothesis is that the sharing of parts is core to one shot learning, but there have been few attempts to test this hypothesis on a large scale. This paper works in the domain of handwritten characters, which contain a rich component structure of strokes. We introduce a generative model of how characters are composed from strokes, and how knowledge from previous characters helps to infer the latent strokes in novel characters. After comparing several models and humans on one shot character learning, we find that our stroke model outperforms a state-of-the-art character model by a large margin, and it provides a closer fit to human perceptual data.
' s personal copy A comparative study on feature reduction approaches in Hindi and Bengali named entity recognition

Features used for named entity recognition (NER) are often high dimensional in nature. These cause overfitting when training data is not sufficient. Dimensionality reduction leads to performance enhancement in such situations. There are a number of approaches for dimensionality reduction based on feature selection and feature extraction. In this paper we perform a comprehensive and comparative study on different dimensionality reduction approaches applied to the NER task. To compare the performance of the various approaches we consider two Indian languages namely Hindi and Bengali. NER accuracies achieved in these languages are comparatively poor as yet, primarily due to scarcity of annotated corpus. For both the languages dimensionality reduction is found to improve performance of the classifiers. A Comparative study of the effectiveness of several dimensionality reduction techniques is presented in detail in this paper.
Sports Celebrity Influence on the Behavioral Intentions of Generation Y Teenagers

Teenagers currently spend an estimated $153 billion a year on everything from computers to cars to clothes (Brand, 2000). Trend conscious teens are very active in utilizing the media and advertising in seeking out the latest products, services, and fashions (Zollo, 1995). A greater understanding of teens' role model influences can help organizations and their advertising agencies more effectively target and communicate to this growing market. In this study, we examine the effect of athlete role models on teenagers' purchase intentions and behaviors. Results from a survey of 218 adolescents are discussed with implications and future research directions for advertising and sports marketing researchers. " When I take my kid out and hit him ground balls at shortstop, he wants to be Derek Jeter. He doesn't want to be me. So any professional athlete who tells you he's not a role model is full of baloney " —Father and Little League Coach (Pulley 2001, p. 131) SPORTS AND SPORTS CELEBRITIES have become major spectacles of today's media culture. Sports celebrities have been looked upon as role models for decades, and with the technological advances in broadcast and interactive media, it appears that famous and infamous athletes are everywhere (Jones and Schumann, 2000). Over the years, advertisers have paid famous athletes millions of dollars to endorse their products. Sports celebri-ties' endorsement responsibilities can range from using or wearing certain brands (e.g., Tiger Woods and Nike apparel) to providing actual brand testimonials (e.g., Rafael Palmiero of the Texas Rangers and Viagra). Some of the most widely utilized advertising spokespersons today are famous athletes such as Despite the widespread use of athletes as product or brand endorsers, there is a relative lack of published research that investigates the influence these athletes may have on a target market. Further , it remains questionable as to whether or not celebrity endorsers really make a difference on the bottom line for advertisers. Miciak and Shanklin (1994) reported that only one out of five commercials that included celebrity endorsers met adver-tisers' strategic expectations. Researchers have concluded that celebrity endorsers may not have " connected " or identified with the intended target market (cf. Sukhdial, Aiken, and Kahle, 2002). One can argue that this may occur because the celebrity endorser lacked identification and perceived familiarity with the target market. Perhaps one of the most important and sought after target markets for advertisers today is one that represents great …
Online Fault Detection and Diagnosis in Photovoltaic Systems Using Wavelet Packets

The nonlinear characteristics of a photovoltaic (PV) array, maximum power point tracking of the PV inverter, presence of blocking diodes, and lower irradiation prevent the conventional protection devices to trip under certain faults and lead to reduced system efficiency and fire hazards. Moreover, the PV characteristics under certain partial shading conditions are similar to that under fault conditions. Hence, it is imperative to detect faults and differentiate faults from the partial shading condition to avoid false tripping of the system. This paper proposes a simple fault detection method using the available data of array voltage and current by means of wavelet packets. The proposed scheme is simulated using MATLAB/Simulink and is experimentally tested on a 1.6-kW $\text{4}\,\times \,\text{4}$ PV array to validate its performance.
Distressed Stocks in Distressed Times

Financially distressed stocks do not underperform healthy stocks when the entire economy is in distress. The asset beta and financial leverage of distressed stocks rise significantly after major market downturns, resulting in a dramatic increase in equity beta. Hence, a long/short healthyminus-distressed trading strategy leads to significant losses when the market rebounds. Managing this risk mitigates the severe losses of financial distress strategies, and significantly improves their Sharpe ratios.
Incremental Learning of Convolutional Neural Networks

Convolutional neural networks provide robust feature extraction with ability to learn complex, highdimensional non-linear mappings from collection of examples. To accommodate new, previously unseen data, without the need of retraining the whole network architecture we introduce an algorithm for incremental learning. This algorithm was inspired by AdaBoost algorithm. It utilizes ensemble of modified convolutional neural networks as classifiers by generating multiple hypotheses. Furthermore, with this algorithm we can work with the confidence score of classification, which can play crucial importance in specific real world tasks. This approach was tested on handwritten numbers classification. The classification error achieved by this approach was highly comparable with non-incremental learning.
Calibration of RGB camera with velodyne LiDAR

Calibration of the LiDAR sensor with RGB camera finds its usage in many application fields from enhancing image classification to the environment perception and mapping. This paper presents a pipeline for mutual pose and orientation estimation of the mentioned sensors using a coarse to fine approach. Previously published methods use multiple views of a known chessboard marker for computing the calibration parameters, or they are limited to the calibration of the sensors with a small mutual displacement only. Our approach presents a novel 3D marker for coarse calibration which can be robustly detected in both the camera image and the LiDAR scan. It also requires only a single pair of camera-LiDAR frames for estimating large sensors displacement. Consequent refinement step searches for more accurate calibration in small subspace of calibration parameters. The paper also presents a novel way for evaluation of the calibration precision using projection error.
Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning

Humans are able to understand and perform complex tasks by strategically structuring the tasks into incremental steps or subgoals. For a robot attempting to learn to perform a sequential task with critical subgoal states, such states can provide a natural opportunity for interaction with a human expert. This paper analyzes the benefit of incorporating a notion of subgoals into Inverse Reinforcement Learning (IRL) with a Human-In-The-Loop (HITL) framework. The learning process is interactive, with a human expert first providing input in the form of full demonstrations along with some subgoal states. These subgoal states define a set of subtasks for the learning agent to complete in order to achieve the final goal. The learning agent queries for partial demonstrations corresponding to each subtask as needed when the agent struggles with the subtask. The proposed Human Interactive IRL (HI-IRL) framework is evaluated on several discrete path-planning tasks. We demonstrate that subgoal-based interactive structuring of the learning task results in significantly more efficient learning, requiring only a fraction of the demonstration data needed for learning the underlying reward function with the baseline IRL model.
Human Motion Reconstruction from Action Video Data Using a 3-Layer-LSTM

Data driven motion generation is a challenging problem. Typically the model is trained using motion capture (MOCAP) data, which is limited in scope and diversity. Solving this problem using motion data extracted from Youtube videos would enable motion generation pipelines to feed from a wider range of action data, allowing for greater variability in the actions generated. Human action is expressed through a sequence of bodily movements. Hence, in this work we explore three stages for the motion synthesis pipeline: first by extracting human pose from video data with Part Affinity Fields method by , and then by training a 3-Layer Recurrent Neural Network (RNN) that learns to generate human-like motion for a given class from annotated video data, and finally we explore a simple synthesis of motion from multiple classes. Ultimately, this approach could enable robust generation of a wide range of motions, capturing the the subtle and fine variations of human movement expressed in videos that populate the Internet to artificially produce life like motion.
Multimedia, hypermedia, and hypertext: Motivation considered and reconsidered

Computer-based instruction (CBI) is becoming increasingly popular in the classroom, particularly because the latest technological advancements allow for visually rich and interactive environments. While the inherent nature of CBIs is often thought to engage learners, research examining the role of motivation in learning with these environments has resulted in mixed findings. These findings are further complicated by unique design characteristics of distinct CBIs. This literature review synthesizes research that has examined the role of theoretically-grounded constructs of motivation in the context of three popular CBIs, multimedia, hypermedia, and hypertext. Specifically, this literature review considered empirical studies that examined the effect of these CBIs on motivation, in addition to the effect of motivation on learning outcomes and the learning process within the context of these environments. The literature review concludes with a theoretical consideration of previous research and a discussion of a framework for future directions.
Resilience and Sustainable Development : Theory of resilience , systems thinking and adaptive governance

 Basic information on SD  Country profiles  Quarterly reports  Policy briefs  Case studies  Conference papers  Workshop papers  Getting in touch with us The European Sustainable Development Network (ESDN) is an informal network of public administrators and other experts who deal with sustainable development strategies and policies. The network covers all 27 EU Member States, plus other European countries. The ESDN is active in promoting sustainable development and facilitating the exchange of good practices in Europe and gives advice to policy-makers at the European and national levels. This ESDN Quarterly Report (QR) provides a condensed overview of the concept of resilience. Despite the complexity of the theory behind resilience, this QR tries to communicate the main notions behind this concept in a way that is understandable and not overly technical. The intention of this QR is to serve as a guide through the concept of resilience. The report does not aim at being exhaustive but intends to provide an overview on the links which are particularly relevant for sustainable development (SD) in general and SD governance in particular. A multitude of diverse sources have been used, mainly from the academic literature. It has to be mentioned the significant and decisive role that the Resilience Alliance has in providing extensive knowledge: the website that they are running, is an exceptionally good source of information for those who are interested and want to deepen their knowledge of resilience. Additionally, among all the scientific publications cited throughout the report, a special mention goes to the book by Walker and Salt (2006) entitled " Resilience thinking: sustaining ecosystems and people in a changing world " , which is very much suggested as a practical source of information on resilience. As a first chapter, an executive summary is provided with a short overview of the report with the essential notions that are depicted throughout the QR. The second chapter then introduces the concept of resilience and gives an extensive background on the notions behind it. It intends to provide guidance, especially to understand the linkages between the concept of resilience and sustainable development, and the importance of resilience and systems thinking for policy-makers and for those who work on SD governance. The third chapter summarizes the relationships among resilience, society, governance and policy. Therefore, the concept of 'adaptive governance' is advanced as a more appropriate way to deal with …
Vision based rail track and switch recognition for self-localization of trains in a rail network

A collision avoidance system for railroad vehicles needs to determine their location in the railroad network precisely and reliably. For a vehicle-based system, that is independent from the infrastructure, it is vital to determine the direction a railroad vehicle turns at switches. In this paper a vision based approach is presented that allows to achieve this reliably, even under difficult conditions. In the images of a camera that observes the area in front of a railroad vehicle the rail tracks are detected in real-time. From the perspective of the moving railroad vehicle rail tracks branch and join from/to the currently travelled rail track. By tracking these rail tracks in the images, switches are detected as they are passed. It is shown that the followed track can be determined at branching switches. The approach is tested with real data from test rides in different locations and under a variety of weather conditions and environments. It proved to be very robust and of high practical use for track-selective self-localization of railroad vehicles, mandatory for collision avoidance.
Monitoring and depth of strategy use in computer-based learning environments for science and history.

Self-regulated learning (SRL) models position metacognitive monitoring as central to SRL processing and predictive of student learning outcomes (Winne & Hadwin, 2008; Zimmerman, 2000). A body of research evidence also indicates that depth of strategy use, ranging from surface to deep processing, is predictive of learning performance.



In this study, we investigated the relationships among the frequency of metacognitive monitoring and the utilization of deep and surface-level strategies, and the connections between these SRL processes and learning outcomes across two academic domains, science and history.


SAMPLE
This was a secondary data analysis of two studies. The first study sample was 170 undergraduate students from a University in the south-eastern United States. The second study sample consisted of 40 US high school students in the same area.



We collected think-aloud protocol SRL and knowledge measure data and conducted both structural equation modelling and path analysis to investigate our research questions.



Findings showed across both studies and two distinct academic domains, students who enacted more frequent monitoring also enacted more frequent deep strategies resulting in better performance on academic evaluations.



These findings suggest the importance of measuring not only what depth of strategies learners use, but also the degree to which they monitor their learning. Attention to both is needed in research and practice.
HydraDB: a resilient RDMA-driven key-value middleware for in-memory cluster computing

In this paper, we describe our experiences and lessons learned from building a general-purpose in-memory key-value middleware, called HydraDB. HydraDB synthesizes a collection of state-of-the-art techniques, including continuous fault-tolerance, Remote Direct Memory Access (RDMA), as well as awareness for multicore systems, etc, to deliver a high-throughput, low-latency access service in a reliable manner for cluster computing applications.
 The uniqueness of HydraDB mainly lies in its design commitment to fully exploit the RDMA protocol to comprehensively optimize various aspects of a general-purpose key-value store, including latency-critical operations, read enhancement, and data replications for high-availability service, etc. At the same time, HydraDB strives to efficiently utilize multicore systems to prevent data manipulation on the servers from curbing the potential of RDMA.
 Many teams in our organization have adopted HydraDB to improve the execution of their cluster computing frameworks, including Hadoop, Spark, Sensemaking analytics, and Call Record Processing. In addition, our performance evaluation with a variety of YCSB workloads also shows that HydraDB can substantially outperform several existing in-memory key-value stores by an order of magnitude. Our detailed performance evaluation further corroborates our design choices.
What is an emerging technology?

There is considerable and growing interest in the emergence of novel technologies, especially from the policy-making perspective. Yet as an area of study, emerging technologies lacks key foundational elements, namely a consensus on what classifies a technology as ’emergent’ and strong research designs that operationalize central theoretical concepts. The present paper aims to fill this gap by developing a definition of ’emerging technologies’ and linking this conceptual effort with the development of a framework for the operationalisation of technological emergence. The definition is developed by combining a basic understanding of the term and in particular the concept of ’emergence’ with a review of key innovation studies dealing with definitional issues of technological emergence. The resulting definition identifies five attributes that feature in the emergence of novel technologies. These are: (i) radical novelty, (ii) relatively fast growth, (iii) coherence, (iv) prominent impact, and (v) uncertainty and ambiguity. The framework for operationalising emerging technologies is then elaborated on the basis of the proposed attributes. To do so, we identify and review major empirical approaches (mainly in, although not limited to, the scientometric domain) for the detection and study of emerging technologies (these include indicators and trend analysis, citation analysis, co-word analysis, overlay mapping, and combinations thereof) and elaborate on how these can be used to operationalise the different attributes of emergence.