nils-herrmann/clean_scirepeval huggingface dataset:
An Agent-based Indoor Wayfinding Based on Digital Sign System

Developing effective urban planning approaches is a major challenge for outdoor and indoor environments. Wayfinding in unfamiliar indoor environments is a prime task which everyone would be encountered. The development of assistive technologies to aid wayfinding is hampered by the lack of reliable and cost-efficient methods providing location information in the environment. We have applied RFID technology as a low-cost and operative approach. Our contribution is the suitable design and placement of digital sign system that can be readily detected by a handheld device. We have simulated the task with an agent-based modeling. The hypothesis of the research is that the wayfinder has a handheld device such as a PDA or a mobile system which receives and responses the signals from digital signs through passive tags. Performance of the simulation showed that the appropriate design of digital signs in an unfamiliar environment would result in a more efficient wayfinding process.
Are Databases Fit for Hybrid Workloads on GPUs? A Storage Engine's Perspective

Employing special-purpose processors (e.g., GPUs) in database systems has been studied throughout the last decade. Research on heterogeneous database systems that use both general-and special-purpose processors has addressed either transaction-or analytic processing, but not the combination of them. Support for hybrid transaction-and analytic processing (HTAP) has been studied exclusively for CPU-only systems. In this paper we ask the question whether current systems are ready for HTAP workload management with cooperating generaland special-purpose processors. For this, we take the perspective of the backbone of database systems: the storage engine. We propose a unified terminology and a comprehensive taxonomy to compare state-of-the-art engines from both domains. We show similarities and differences, and determine a necessary set of features for engines supporting HTAP workload on CPUs and GPUs. Answering our research question, our findings yield a resolute: not yet.
Effective Use of Synthetic Data for Urban Scene Semantic Segmentation

Training a deep network to perform semantic segmentation requires large amounts of labeled data. To alleviate the manual effort of annotating real images, researchers have investigated the use of synthetic data, which can be labeled automatically. Unfortunately, a network trained on synthetic data performs relatively poorly on real images. While this can be addressed by domain adaptation, existing methods all require having access to real images during training. In this paper, we introduce a drastically different way to handle synthetic images that does not require seeing any real images at training time. Our approach builds on the observation that foreground and background classes are not affected in the same manner by the domain shift, and thus should be treated differently. In particular, the former should be handled in a detection-based manner to better account for the fact that, while their texture in synthetic images is not photo-realistic, their shape looks natural. Our experiments evidence the effectiveness of our approach on Cityscapes and CamVid with models trained on synthetic data only.
The nature of cyberbullying, and strategies for prevention

0747-5632/$ see front matter 2012  A http://dx.doi.org/10.1016/j.chb.2012.05.024 ⇑ Corresponding author.  Unit for School an University of London, UK. Tel.: +44 (0)207 919 7870; E-mail address: r.slonje@gmail.com (R. Slonje). Cyberbullying has been identified as an important problem amongst youth in the last decade. This paper reviews some recent findings and discusses general concepts within the area. The review covers definitional issues such as repetition and power imbalance, types of cyberbullying, age and gender differences, overlap with traditional bullying and sequence of events, differences between cyberbullying and traditional bullying, motives for and impact of cyber victimization, coping strategies, and prevention/intervention possibilities. These issues will be illustrated by reference to recent and current literature, and also by in-depth interviews with nine Swedish students aged 13–15 years, who had some first-hand experience of one or more cyberbullying episodes. We conclude by discussing the evidence for different coping, intervention and prevention strategies.
Modeling of Item-Difficulty for Ontology-based MCQs

Multiple choice questions (MCQs) that can be generated from a domain ontology can significantly reduce human effort & time re quired for authoring & administering assessments in an e-Learning envir o ment. Even though there are various methods for generating MCQs from ontologi es, methods for determining the difficulty-levels of such MCQs are less explor ed. In this paper, we study various aspects and factors that are involved in deter mining the difficultyscore of an MCQ, and propose an ontology-based model for the p rediction. This model characterizes the difficulty values associated with t he s em and choice set of the MCQs, and describes a measure which combines both the s cor s. Furthermore, the notion of assigning difficultly-scores based on th e skill level of the test taker is utilized for predicating difficulty-score of a stem . We studied the effectiveness of the predicted difficulty-scores with the help of a psychometric model from the Item Response Theory, by involving real-students a d domain experts. Our results show that, the predicated difficulty-levels of t he MCQs are having high correlation with their actual difficulty-levels.
Corporate Social Responsibility and Innovation in Management Accounting

This study develops a conceptual framework to illustrate how sustainability issues are embedded in the management control system to operationalise firms' corporate social responsibility (CSR) objectives and strategies. • Categorising firms' CSR activities into Responsive and Strategic CSR agendas (Porter and Kramer 2006), this framework emphasises that different uses of MCS must be adopted to effectively operationalise different CSR agendas. • Boundary systems and diagnostic uses of budgets and performance management systems are more pertinent to operationalising Responsive CSR agendas, whereas the belief system and interactive uses of MCS are more effective in facilitating the selection and implementation of Strategic CSR programmes. • This study provides empirical evidence on the different types of Responsive and Strategic CSR programmes in two Chinese State-owned Enterprises (SOEs). Our empirical data shed light on the role of MCS in implementing CSR strategies at these firms.
Linking CRM Strategy , Customer Performance Measures and Performance in the Hotel Industry

Customer relationship management (CRM) has been increasingly adopted because of its benefits of greater customer satisfaction and loyalty, which in turn, leads to enhanced financial and competitive performance. This paper reports on a study that examines the relationship between CRM strategy and performance and determines whether the use of customer performance measures plays a mediating role in the relationship between CRM strategy and performance. This study contributes to the limited literature on CRM strategy since little is known about the use of CRM strategy and customer performance measures and their relation with performance in the hotel industry in Malaysia. Data were collected through a questionnaire survey of hotels in Malaysia. Hierarchical regression analyses on a sample of 95 hotels revealed that only the information technology dimension of CRM strategy has a significant and positive effect on performance. In addition, the hypothesis concerning the role of customer performance measures as a mediator was supported.
Image-guided full waveform inversion

Multiple problems, including high computational cost, spurious local minima, and solutions with no geologic sense, have prevented widespread application of full waveform inversion (FWI), especially FWI of seismic reflections. These problems are fundamentally related to a large number of model parameters and to the absence of low frequencies in recorded seismograms. Instead of inverting for all the parameters in a dense model, image-guided full waveform inversion inverts for a sparse model space that contains far fewer parameters. We represent a model with a sparse set of values, and from these values, we use image-guided interpolation (IGI) and its adjoint operator to compute finelyand uniformly-sampled models that can fit recorded data in FWI. Because of this sparse representation, image-guided FWI updates more blocky models, and this blockiness in the model space mitigates the absence of low frequencies in recorded data. Moreover, IGI honors imaged structures, so image-guided FWI built in this way yields models that are geologically sensible.
Use of concurrent pupil dilation assessment to inform interpretation and analysis of fMRI data

Potential contributions of concurrently acquired pupil dilation data to functional magnetic resonance imaging (fMRI) experiments were examined. Sixteen healthy participants completed a working memory task (digit sorting) during measurement of pupil dilation outside the fMRI environment and during concurrent 3T fMRI assessment. Pupil dilation increased parametrically with task difficulty inside and outside the scanner, on a similar time course, suggesting that task demand was similar in both environments. The time course of pupil dilation during digit sorting was similar to the time course of the fMRI signal in the middle frontal gyrus, suggesting that middle-frontal gyrus activity indexed the engagement working memory processes. Incorporating individual differences in pupil dilation improved the sensitivity and specificity of general linear modeling analyses of activity in the middle frontal gyrus, above and beyond standard analytic techniques. Results suggest concurrent pupil dilation during fMRI assessment can help to (1) specify whether task demand is the same inside and outside the fMRI environment, (2) resolve the extent to which fMRI signals reflect different aspects of event-related designs, and (3) explain variation in fMRI data due to individual differences in information processing.
Computationally efficient leakage inductance calculation for a high-frequency core-type transformer

Leakage inductance is a critical design attribute of high-frequency transformers. In this paper, a new method to estimate core-type transformer leakage inductance is proposed. The magnetic analysis uses a combination of analytical and numerical analysis. A two-dimensional magnetic analysis using Biot-Savart law and the method of images is introduced. Using the same field analysis, expressions for mean squared field intensity values necessary for estimating proximity effect loss in the windings are also derived.
Quantitative Network Monitoring with NetQRE

In network management today, dynamic updates are required for traffic engineering and for timely response to security threats. Decisions for such updates are based on monitoring network traffic to compute numerical quantities based on a variety of network and application-level performance metrics. Today's state-of-the-art tools lack programming abstractions that capture application or session-layer semantics, and thus require network operators to specify and reason about complex state machines and interactions across layers. To address this limitation, we present the design and implementation of NetQRE, a high-level declarative toolkit that aims to simplify the specification and implementation of such quantitative network policies. NetQRE integrates regular-expression-like pattern matching at flow-level as well as application-level payloads with aggregation operations such as sum and average counts. We describe a compiler for NetQRE that automatically generates an efficient implementation with low memory footprint. Our evaluation results demonstrate that NetQRE allows natural specification of a wide range of quantitative network tasks ranging from detecting security attacks to enforcing application-layer network management policies. NetQRE results in high performance that is comparable with optimized manually-written low-level code and is significantly more efficient than alternative solutions, and can provide timely enforcement of network policies that require quantitative network monitoring.
Self-Oscillating Fluxgate Current Sensor with Pulse Width Modulated Feedback

—Several methods, based on the relay and phase-locked loop (PLL) experiment for determining the ultimate frequency, ultimate gain, and Nyquist curve Gp(iω), given arg(Gp(iω)), are analyzed. To support the practical application of these algorithms in the presence of the higher harmonics, measurement noise, and load disturbances, an adaptive filter (AF) of the band-pass type has been applied. The methods are tested on a large class of typical industrial processes. The SIMULINK realizations of the algorithms are presented.
Half-Mode Substrate Integrated Waveguide (HMSIW) Leaky-Wave Antenna for Millimeter-Wave Applications

A novel leaky-wave antenna is demonstrated and developed at Ka-band in this work based on the newly proposed half-mode substrate integrated waveguide (HWSIW). This antenna is accurately simulated by using a full-wave electromagnetic simulator and then fabricated through a single-layer printed circuit board (PCB) process. Wide bandwidth and a quasi-omnidirectional radiation pattern are obtained. The proposed antenna is therefore a good candidate for millimeter-wave applications. Measured results are in good agreement with simulated results.
Accelerating Encryption Algorithms Using Parallelism

The importance of protecting the information has increased rapidly during the last decades and as a consequence so did the need for cryptographic algorithms. We want to increase the speed of these methods that protect our data as much as we can. In this paper , we used parallelism for encryption algorithms and I/O operations to emphasize the benefit of it, by implementing and improving two cryptographic modes such as Cipher Block Chaining (CBC ) and Interleaved CBC (ICBC ) for Advanced Encryption Algorithm (AES ). The result of this paper is the remarkable speedup in encryption and decryption time of cryptographic systems when using parallel paradigms (OpenCL, Cuda, OpenMP) and the comparison of the serial versus parallel implementation.
SEMI-AUTOMATIC EXTRACTION OF RIBBON ROADS FORM HIGH RESOLUTION REMOTELY SENSED IMAGERY BY COOPERATION BETWEEN ANGULAR TEXTURE SIGNATURE AND TEMPLATE MATCHING

Road tracking is a promising technique to increase the efficiency of road mapping. In this paper an improved road tracker, based on cooperation between angular texture signature and template matching, is presented. Our tracker uses parabola to model the road trajectory and to predict the position of next road centreline point. It employs angular texture signature to get the exact moving direction of current road centreline point, and moves forward one predefined step along the direction to reach a new position, and then uses curvature change to verify the new added road point whether right enough. We also build compactness of angular texture signature polygon to check whether the angular texture signature is suitable to be used to go on tracking. When angular texture signature fails, least squares template matching is then employed instead. Cooperation between angular texture signature and template matching can reliably extract continuous and homogenous ribbon roads on high resolution remotely sensed imagery. ∗ Corresponding author
Parallel Stochastic Gradient Descent with Sound Combiners

Stochastic gradient descent (SGD) is a wellknown method for regression and classification tasks. However, it is an inherently sequential algorithm—at each step, the processing of the current example depends on the parameters learned from the previous examples. Prior approaches to parallelizing linear learners using SGD, such as HOGWILD! and ALLREDUCE, do not honor these dependencies across threads and thus can potentially suffer poor convergence rates and/or poor scalability. This paper proposes SYMSGD, a parallel SGD algorithm that, to a first-order approximation, retains the sequential semantics of SGD. Each thread learns a local model in addition to a model combiner, which allows local models to be combined to produce the same result as what a sequential SGD would have produced. This paper evaluates SYMSGD’s accuracy and performance on 6 datasets on a shared-memory machine shows upto 11× speedup over our heavily optimized sequential baseline on 16 cores and 2.2×, on average, faster than HOGWILD!.
Expert-Guided Generative Topographical Modeling with Visual to Parametric Interaction

Introduced by Bishop et al. in 1996, Generative Topographic Mapping (GTM) is a powerful nonlinear latent variable modeling approach for visualizing high-dimensional data. It has shown useful when typical linear methods fail. However, GTM still suffers from drawbacks. Its complex parameterization of data make GTM hard to fit and sensitive to slight changes in the model. For this reason, we extend GTM to a visual analytics framework so that users may guide the parameterization and assess the data from multiple GTM perspectives. Specifically, we develop the theory and methods for Visual to Parametric Interaction (V2PI) with data using GTM visualizations. The result is a dynamic version of GTM that fosters data exploration. We refer to the new version as V2PI-GTM. In this paper, we develop V2PI-GTM in stages and demonstrate its benefits within the context of a text mining case study.
Local Deep Neural Networks for Age and Gender Classification

Local deep neural networks have been recently introduced for gender recognition. Although, they achieve very good performance they are very computationally expensive to train. In this work, we introduce a simplified version of local deep neural networks which significantly reduces the training time. Instead of using hundreds of patches per image, as suggested by the original method, we propose to use 9 overlapping patches per image which cover the entire face region. This results in a much reduced training time, since just 9 patches are extracted per image instead of hundreds, at the expense of a slightly reduced performance. We tested the proposed modified local deep neural networks approach on the LFW and Adience databases for the task of gender and age classification. For both tasks and both databases the performance is up to 1% lower compared to the original version of the algorithm. We have also investigated which patches are more discriminative for age and gender classification. It turns out that the mouth and eyes regions are useful for age classification, whereas just the eye region is useful for gender classification.
Intra-class Variation Isolation in Conditional GANs

Current state-of-the-art conditional generative adversarial networks (C-GANs) require strong supervision via labeled datasets in order to generate images with continuously adjustable, disentangled semantics. In this paper we introduce a new formulation of the C-GAN that is able to learn realistic models with continuous, semantically meaningful input parameters and which has the advantage of requiring only the weak supervision of binary attribute labels. We coin the method intra-class variation isolation (IVI) and the resulting network the IVI-GAN. The method allows continuous control over the attributes in synthesised images where precise labels are not readily available. For example, given only labels found using a simple classifier of ambient / non-ambient lighting in images, IVI has enabled us to learn a generative face-image model with controllable lighting that is disentangled from other factors in the synthesised images, such as the identity. We evaluate IVI-GAN on the CelebA and CelebA-HQ datasets, learning to disentangle attributes such as lighting, pose, expression and age, and provide a quantitative comparison of IVI-GAN with a classical continuous C-GAN.
Ant colony optimization for routing and load-balancing: survey and new directions

Although an ant is a simple creature, collectively a colony of ants performs useful tasks such as finding the shortest path to a food source and sharing this information with other ants by depositing pheromone. In the field ofant colony optimization (ACO), models ofcollective intelligenceof ants are transformed into useful optimization techniques that find applications in computer networking. In this survey, the problem-solving paradigm of ACO is explicated and compared to traditional routing algorithms along the issues of routing information, routing overhead and adaptivity. The contributions of this survey include 1) providing a comparison and critique of the state-of-the-art approaches for mitigatingstagnation (a major problem in many ACO algorithms), 2) surveying and comparing three major research in applying ACO in routing and load-balancing, and 3) discussing new directions and identifying open problems. The approaches for mitigating stagnation discussed include:evaporation, aging, pheromone smoothingand limiting, privileged pheromone layingandpheromone-heuristic control . The survey on ACO in routing/load-balancing includes comparison and critique of ant-based control and its ramifications, AntNetand its extensions, as well as ASGAand SynthECA. Discussions on new directions include an ongoing work of the authors in applyingmultiple ant colony optimizationin load-balancing.
AtlanticWave-SDX : An International SDX to Support Science Data Applications

New scientific instruments that are being designed and deployed in the coming years will dramatically increase the need for large, real-time data transfers among scientists throughout the world. One such instrument is the Large Synoptic Survey Telescope being built in Chile that will produce 6.4 GB images every 17 seconds. This paper describes an ongoing effort to meet the demands of these large data scientific instruments through the development of an international software defined exchange point (SDX) that will meet the provisioning needs for the scientific users. The specific planned and ongoing work in SDX architecture is described with specific consideration for policy specification and security. Software Defined Exchange;
KEPLER: Keypoint and Pose Estimation of Unconstrained Faces by Learning Efficient H-CNN Regressors

Keypoint detection is one of the most importantpre-processing steps in tasks such as face modeling, recognitionand verification. In this paper, we present an iterative methodfor Keypoint Estimation and Pose prediction of unconstrainedfaces by Learning Efficient H-CNN Regressors (KEPLER) foraddressing the face alignment problem. Recent state of the artmethods have shown improvements in face keypoint detectionby employing Convolution Neural Networks (CNNs). Althougha simple feed forward neural network can learn the mappingbetween input and output spaces, it cannot learn the inherentstructural dependencies. We present a novel architecture calledH-CNN (Heatmap-CNN) which captures structured global andlocal features and thus favors accurate keypoint detecion. H-CNNis jointly trained on the visibility, fiducials and 3D-pose of theface. As the iterations proceed, the error decreases making thegradients small and thus requiring efficient training of DCNNs tomitigate this. KEPLER performs global corrections in pose andfiducials for the first four iterations followed by local correctionsin a subsequent stage. As a by-product, KEPLER also provides3D pose (pitch, yaw and roll) of the face accurately. In thispaper, we show that without using any 3D information, KEPLERoutperforms state of the art methods for alignment on challengingdatasets such as AFW [38] and AFLW .