nils-herrmann/clean_scirepeval huggingface dataset:
4 A Theory of Graph Comprehension

Venn diagrams, flow charts, tree structures, node networks, to name just a few-or to the great lengths that computer companies go to advertise the graphic capabilities of their products, to see that charts and graphs have enormous appeal to people. All of this is true despite the fact that in virtually every case, the same information can be communicated by nonpictorial means: tables of numbers, lists of propositions cross-referI ! enced by global variables, labeled bracketings, and so on. Perhaps pictorial displays are simply pleasing to the eye, but both introspection and experimental evidence (Carter, 1947; Culbertson & Powers, 1959; Schutz, l%la, I
Determinants of Online Social Business Network Usage Behavior — Applying the Technology Acceptance Model and Its Extensions

Usage of online social business networks like LinkedIn and XING have become commonplace in today’s workplace. This research addresses the question of what factors drive the intention to use online social business networks. Theoretical frame of the study is the Technology Acceptance Model (TAM) and its extensions, most importantly the TAM2 model. Data has been collected via a Web Survey among users of LinkedIn and XING from January to April 2010. Of 541 initial responders 321 finished the questionnaire. Operationalization was tested using confirmatory factor analyses and causal hypotheses were evaluated by means of structural equation modeling. Core result is that the TAM2 model generally holds in the case of online social business network usage behavior, explaining 73% of the observed usage intention. This intention is most importantly driven by perceived usefulness, attitude towards usage and social norm, with the latter effecting both directly and indirectly over perceived usefulness. However, perceived ease of use has—contrary to hypothesis—no direct effect on the attitude towards usage of online social business networks. Social norm has a strong indirect influence via perceived usefulness on attitude and intention, creating a network effect for peer users. The results of this research provide implications for online social business network design and marketing. Customers seem to evaluate ease of use as an integral part of the usefulness of such a service which leads to a situation where it cannot be dealt with separately by a service provider. Furthermore, the strong direct impact of social norm implies application of viral and peerto-peer marketing techniques while it’s also strong indirect effect implies the presence of a network effect which stabilizes the ecosystem of online social business service vendors.
Bounding the Maximum Length of Non-preemptive Regions under Fixed Priority Scheduling

The question whether preemptive systems are better than non-preemptive systems has been debated for a long time, but only partial answers have been provided in the real-time literature and still some issues remain open. In fact, each approach has advantages and disadvantages, and no one dominates the other when both predictability and efficiency have to be taken into account in the system design. In particular, limiting preemptions allows increasing program locality, making timing analysis more predictable with respect to the fully preemptive case. In this paper, we integrate the features of both preemptive and non-preemptive scheduling by considering that each task can switch to non-preemptive mode, at any time, for a bounded interval. Three methods (with different complexity and performance) are presented to calculate the longest non-preemptive interval that can be executed by each task, under fixed priorities, without degrading the schedulability of the task set, with respect to the fully preemptive case. The methods are also compared by simulations to evaluate their effectiveness in reducing the number of preemptions.
Generic Attacks on Secure Outsourced Databases

Recently, various protocols have been proposed for securely outsourcing database storage to a third party server, ranging from systems with "full-fledged" security based on strong cryptographic primitives such as fully homomorphic encryption or oblivious RAM, to more practical implementations based on searchable symmetric encryption or even on deterministic and order-preserving encryption. On the flip side, various attacks have emerged that show that for some of these protocols confidentiality of the data can be compromised, usually given certain auxiliary information. We take a step back and identify a need for a formal understanding of the inherent efficiency/privacy trade-off in outsourced database systems, independent of the details of the system. We propose abstract models that capture secure outsourced storage systems in sufficient generality, and identify two basic sources of leakage, namely access pattern and ommunication volume. We use our models to distinguish certain classes of outsourced database systems that have been proposed, and deduce that all of them exhibit at least one of these leakage sources.
 We then develop generic reconstruction attacks on any system supporting range queries where either access pattern or communication volume is leaked. These attacks are in a rather weak passive adversarial model, where the untrusted server knows only the underlying query distribution. In particular, to perform our attack the server need not have any prior knowledge about the data, and need not know any of the issued queries nor their results. Yet, the server can reconstruct the secret attribute of every record in the database after about $N^4$ queries, where N is the domain size. We provide a matching lower bound showing that our attacks are essentially optimal. Our reconstruction attacks using communication volume apply even to systems based on homomorphic encryption or oblivious RAM in the natural way.
 Finally, we provide experimental results demonstrating the efficacy of our attacks on real datasets with a variety of different features. On all these datasets, after the required number of queries our attacks successfully recovered the secret attributes of every record in at most a few seconds.
BALANCED REALIZATION AND MODEL REDUCTION FOR UNSTABLE SYSTEMS

This paper proposes a new balanced realization and model reduction method for possibly unstable systems by introducing some new controllability and observability Gramians. These Gramians can be related to minimum control energy and minimum estimation error. In contrast to Gramians defined in the literature for unstable systems, these Gramians can always be computed for systems without imaginary axis poles and they reduce to the standard controllability and observability Gramians when the systems are stable. The proposed balanced model reduction method enjoys the similar error bounds as does for the standard balanced model reduction. Furthermore, the new error bounds and the actual approximation errors seem to be much smaller than the ones using the methods given in the literature for unstable systems.
A compact and efficient image retrieval approach based on border/interior pixel classification

This paper presents \bic (Border/Interior pixel Classification), a compact and efficient CBIR approach suitable for broad image domains. It has three main components: (1) a simple and powerful image analysis algorithm that classifies image pixels as either border or interior, (2) a new logarithmic distance (dLog) for comparing histograms, and (3) a compact representation for the visual features extracted from images. Experimental results show that the BIC approach is consistently more compact, more efficient and more effective than state-of-the-art CBIR approaches based on sophisticated image analysis algorithms and complex distance functions. It was also observed that the dLog distance function has two main advantages over vectorial distances (e.g., L1): (1) it is able to increase substantially the effectiveness of (several) histogram-based CBIR approaches and, at the same time, (2) it reduces by 50% the space requirement to represent a histogram.
Deep Coevolutionary Network: Embedding User and Item Features for Recommendation

Recommender systems oen use latent features to explain the behaviors of users and capture the properties of items. As users interact with dierent items over time, user and item features can inuence each other, evolve and co-evolve over time. e compatibility of user and item’s feature further inuence the future interaction between users and items. Recently, point process based models have been proposed in the literature aiming to capture the temporally evolving nature of these latent features. However, these models oen make strong parametric assumptions about the evolution process of the user and item latent features, which may not reect the reality, and has limited power in expressing the complex and nonlinear dynamics underlying these processes. To address these limitations, we propose a novel deep coevolutionary network model (DeepCoevolve), for learning user and item features based on their interaction graph. DeepCoevolve use recurrent neural network (RNN) over evolving networks to dene the intensity function in point processes, which allows the model to capture complex mutual inuence between users and items, and the feature evolution over time. We also develop an ecient procedure for training the model parameters, and show that the learned models lead to signicant improvements in recommendation and activity prediction compared to previous state-of-the-arts parametric models.
Coordinated multipoint transmission/reception techniques for LTE-advanced [Coordinated and Distributed MIMO]

This article presents an elaborate coordination technique among multiple cell sites called coordinated multipoint transmission and reception in the Third Generation Partnership Project for LTE-Advanced. After addressing major radio access techniques in the LTE Release 8 specifications, system requirements and applied radio access techniques that satisfy the requirements for LTE-Advanced are described including CoMP transmission and reception. Then CoMP transmission and reception schemes and the related radio interface, which were agreed upon or are currently being discussed in the 3GPP, are presented. Finally, system-level simulation evaluations show that the CoMP transmission and reception schemes have a significant effect in terms of improving the cell edge user throughput based on LTE-Advanced simulation conditions.
Extended Boolean Information Retrieval

Gerard Salton has worked on a number of non-numeric computer applications including automatic information retrieval. He has published a large number of articles and several books on information retrieval, most recently, "Introduction to Modern Information Retrieval," 1983. Edward Fox is currently assistant professor of computer science at VPI. Harry Wu is currently involved in the comparative study of relational database systems. His main interest is in information storage and retrieval. Authors' Present Addresses: G. Salton, Dept. of Computer Science, Cornell Univ., Ithaca, NY 14853; E. A. Fox, Virginia Polytechnic Institute and State Univ., Blacksburg, VA 24061; H. Wu, ITT--Programming Technology Center, lO00 Oronogue Lane, Stratford, CT 06497. This study was supported in part by the National Science Foundation under Grant IST-8108696. Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission.
Analysis of Datamining Technique for Traffic Accident Severity Problem : A Review

This paper is discussing about the road accident severity survey using data mining, where different approaches have been considered. We have collected research work carried out by different researchers based on road accidents. Article describing the review work in context of road accident case’s using data mining approach. The article is consisting of collections of methods in different scenario with the aim to resolve the road accident. Every method is somewhere seeming to productive in some ways to decrease the no of causality. It will give a better edge to different country where the no of accidents is leading to fatality of life.
Combining Argument Mining Techniques

In this paper, we look at three different methods of extracting the argumentative structure from a piece of natural language text. These methods cover linguistic features, changes in the topic being discussed and a supervised machine learning approach to identify the components of argumentation schemes, patterns of human reasoning which have been detailed extensively in philosophy and psychology. For each of these approaches we achieve results comparable to those previously reported, whilst at the same time achieving a more detailed argument structure. Finally, we use the results from these individual techniques to apply them in combination, further improving the argument structure identification.
Scaffolding and Achievement in Problem-Based and Inquiry Learning : A Response to Kirschner , Sweller , and Clark ( 2006 )

Many innovative approaches to education such as problem-based learning (PBL) and inquiry learning (IL) situate learning in problem-solving or investigations of complex phenomena. Kirschner, Sweller, and Clark (2006) grouped these approaches together with unguided discovery learning. However, the problem with their line of argument is that IL and PBL approaches are highly scaffolded. In this article, we first demonstrate that Kirschner et al. have mistakenly conflated PBL and IL with discovery learning. We then present evidence demonstrating that PBL and IL are powerful and effective models of learning. Far from being contrary to many of the principles of guided learning that Kirschner et al. discussed, both PBL and IL employ scaffolding extensively thereby reducing the cognitive load and allowing students to learn in complex domains. Moreover, these approaches to learning address important goals of education that include content knowledge, epistemic practices, and soft skills such as collaboration and self-directed learning.
A new pedestrian dataset for supervised learning

This paper presents a comparative analysis of different pedestrian dataset characteristics. The main goal of the research is to determine what characteristics are desirable for improved training and validation of pedestrian detectors and classifiers. The work focuses on those aspects of the dataset which affect classification success using the most common boosting methods. Dataset characteristics such as image size, aspect ratio, geometric variance and the relative scale of positive class instances (pedestrians) within the training window form an integral part of classification success. This paper will examine the effects of varying these dataset characteristics with a view to determining the recommended attributes of a high quality and challenging dataset. While the primary focus is on characteristics of the positive training dataset, some discussion of desirable attributes for the negative dataset is important and is therefore included. This paper also serves to publish our current pedestrian dataset in various forms for non-commercial use by the scientific community. We believe the published dataset to be one of the largest, most flexible, and representative datasets available for pedestrian/person detection tasks.
Business Process Analytics Using a Big Data Approach

Continuous improvement of business processes is a challenging task that requires complex and robust supporting systems. Using advanced analytics methods and emerging technologies--such as business intelligence systems, business activity monitoring, predictive analytics, behavioral pattern recognition, and "type simulations"--can help business users continuously improve their processes. However, the high volumes of event data produced by the execution of processes during the business lifetime prevent business users from efficiently accessing timely analytics data. This article presents a technological solution using a big data approach to provide business analysts with visibility on distributed process and business performance. The proposed architecture lets users analyze business performance in highly distributed environments with a short time response. This article is part of a special issue on leveraging big data and business analytics.
Audience empathy: a phenomenological method for mediated performance

This research investigates audience experience of empathy with a performer during a digitally mediated performance. Theatrical performance necessitates social interaction between performers and audience. We present a performance-based study that explores audience awareness of performer's kinaesthetic activity in 2 ways: by isolating the audience's senses (visual, auditory, and kinaesthetic) and by focusing audience perception through defamiliarization. By positioning the performer behind the audience: in their 'backspace', we focus the audience's attention to the performer in an unfamiliar way. We describe two research contributions to the study of audience empathic experience during performance. The first is the development of a phenomenological interview method designed for extracting empirical evaluations of experience of audience members in a performance scenario. The second is a descriptive model for a poetics of reception. Our model is based on an empathetic audience-performer relationship that includes 3 components of audience awareness: contextual, interpersonal, and sense-based. Our research contributions are of particular benefit to performances involving digital media, and can provide insight into audience experience of empathy.
Learning a Sparse Representation for Object Detection

We present an approach for learning to detect objects in still gray images, that is based on a sparse, part-based representation of objects. A vocabulary of information-rich object parts is automatically constructed from a set of sample images of the object class of interest. Images are then represented using parts from this vocabulary, along with spatial relations observed among them. Based on this representation, a feature-efficient learning algorithm is used to learn to detect instances of the object class. The framework developed can be applied to any object with distinguishable parts in a relatively fixed spatial configuration. We report experiments on images of side views of cars. Our experiments show that the method achieves high detection accuracy on a difficult test set of real-world images, and is highly robust to partial occlusion and background variation. In addition, we discuss and offer solutions to several methodological issues that are significant for the research community to be able to evaluate object detection
Citizen participation, perceived control, and psychological empowerment.

The research integrates the citizen participation literature with research on perceived control in an effort to further our understanding of psychological empowerment. Eleven indices of empowerment representing personality, cognitive, and motivational measures were identified to represent the construct. Three studies examined the relationship between empowerment and participation. The first study examined differences among groups identified by a laboratory manipulation as willing to participate in personally relevant or community relevant situations. Study II examined differences for groups defined by actual involvement in community activities and organizations. Study III replicated Study II with a different population. In each study, individuals reporting a greater amount of participation scored higher on indices of empowerment. Psychological empowerment could be described as the connection between a sense of personal competence, a desire for, and a willingness to take action in the public domain. Discriminant function analyses resulted in one significant dimension, identified as pyschological empowerment, that was positively correlated with leadership and negatively correlated with alienation.
A vision-guided autonomous quadrotor in an air-ground multi-robot system

In this paper, a vision-guided autonomous quadrotor in an air-ground multi-robot system has been proposed. This quadrotor is equipped with a monocular camera, IMUs and a flight computer, which enables autonomous flights. Two complementary pose/motion estimation methods, respectively marker-based and optical-flow-based, are developed by considering different altitudes in a flight. To achieve smooth take-off, stable tracking and safe landing with respect to a moving ground robot and desired trajectories, appropriate controllers are designed. Additionally, data synchronization and time delay compensation are applied to improve the system performance. Real-time experiments are conducted in both indoor and outdoor environments.
Monocular reconstruction of vehicles: Combining SLAM with shape priors

Reasoning about objects in images and videos using 3D representations is re-emerging as a popular paradigm in computer vision. Specifically, in the context of scene understanding for roads, 3D vehicle detection and tracking from monocular videos still needs a lot of attention to enable practical applications. Current approaches leverage two kinds of information to deal with the vehicle detection and tracking problem: (1) 3D representations (eg. wireframe models or voxel based or CAD models) for diverse vehicle skeletal structures learnt from data, and (2) classifiers trained to detect vehicles or vehicle parts in single images built on top of a basic feature extraction step. In this paper, we propose to extend current approaches in two ways. First, we extend detection to a multiple view setting. We show that leveraging information given by feature or part detectors in multiple images can lead to more accurate detection results than single image detection. Secondly, we show that given multiple images of a vehicle, we can also leverage 3D information from the scene generated using a unique structure from motion algorithm. This helps us localize the vehicle in 3D, and constrain the parameters of optimization for fitting the 3D model to image data. We show results on the KITTI dataset, and demonstrate superior results compared with recent state-of-the-art methods, with upto 14.64 % improvement in localization error.
Computationally Efficient Winding Loss Calculation with Multiple Windings , Arbitrary Waveforms , and Two-Dimensional or Three-Dimensional Field Geometry

The squared-field-derivative method for calculating eddy-current (proximity-effect) losses in round-wire or litz-wire transformer and inductor windings is derived. The method is capable of analyzing losses due to two-dimensional and three-dimensional field effects in multiple windings with arbitrary waveforms in each winding. It uses a simple set of numerical magnetostatic field calculations, which require orders of magnitude less computation time than numerical eddy-current solutions, to derive a frequency-independent matrix describing the transformer or inductor. This is combined with a second, independently calculated matrix, based on derivatives of winding currents, to compute total ac loss. Experiments confirm the accuracy of the method.