nils-herrmann/clean_scirepeval huggingface dataset:
Designing Virtual Worlds as Architecture 1 Designing Virtual Worlds as Architecture

Designing virtual worlds as architecture considers the form and function of spatial virtual environments as an alternative kind of architectural design. This perspective can lead to a new way to organize and explore the Internet as a place. In this paper, we refer to this type of online environment as virtual architecture. Traditionally, virtual architecture refers to the 3D visual simulation of physical architecture. This purpose of virtual architecture has been widely recognised and applied to architectural practice. Virtual architecture as an online functional place (Maher et al, 2000) still struggles at the research level due to the lack of understanding of the materials for designing virtual architecture and the lack of appropriate design platforms or tools. The design of virtual worlds such as virtual design studios (Maher and Simoff, 1999), virtual offices (Maher et al, 1999) and virtual meeting environments provide a basis for studying design materials for virtual architecture. These designs distinguish themselves as functional virtual places that provide a sense of place and a sense of awareness of others. Through these designs, we can generalise the effect of software as the design materials of virtual architecture. This paper explores the nature of these new design materials, evaluates the current status of various design platforms or tools and considers the future development of virtual architecture as a functional place. Virtual architecture (VA) is an online place designed using the metaphor of physical architecture. As the focus in this paper, firstly virtual architecture is a functional place for housing online activities and carrying electronic information. Most commonly these are achieved through computer programming languages, which can be called functional core (FC). Virtual architecture inhabits in the digital domain and is presented to people as certain kinds of digital media, which can be called visual shell (VS). Similar to physical architecture, virtual architecture consists of different building elements. In an object-oriented environment, each of these elements is represented as an object. They can be generalised as design materials (DM). Hence each object of these design materials should also have its sub functional core and sub visual shell as parts of the functional core and visual shell of virtual architecture. These ideas are illustrated in
Mental Models Concepts for System Dynamics Research

Although "mental models" are of central importance to system dynamics research and practice, the field has yet to develop an unambiguous and agreed upon definition of them. To begin to address this problem, existing definitions and descriptions of mental models in system dynamics and several literatures related to cognitive science were reviewed and compared. Available definitions were found to be overly brief, general, and vague, and different authors were found to markedly disagree on the basic characteristics of mental models. Based on this review, we conc luded that in order to reduce the amount of confusion in the literature, the mental models concept should be "unbundled" and the term "mental models" should be used more narrowly. To initiate a dialogue through which the system dynamics community might achieve a shared understanding of mental models, we proposed a new definition of "mental models of dynamic systems" accompanied by an extended annotation that explains the definitional choices made and suggests terms for other cognitive structures left undefined by narrowing the mental model concept. Suggestions for future research that could improve the field's ability to further define mental models are discussed. 3 A difficulty for those who want to understand or to appraise mental models is that their proponents seem to have somewhat different views. Although the phrase "mental models" is ubiquitous in the literature, there are surprisingly few explicit definitions of them.
A Novel Autologous Scaffold for Diced-cartilage Grafts in Dorsal Augmentation Rhinoplasty

Diced-cartilage grafts have been used for dorsal nasal augmentation for several years with good results. However, compounds such as Surgicel and temporalis fascia used as a wrap have inherent problems associated with them, predominantly inflammation and graft resorption. An autologous carrier could provide stabilization of cartilage grafts while avoiding the complications seen with earlier techniques. In our patients, a malleable construct was used for dorsal nasal augmentation in which autologous diced-cartilage grafts were stabilized with autologous tissue glue (ATG) created from platelet-rich plasma (platelet gel) and platelet-poor plasma (fibrin glue). A prospective analysis of 68 patients, who underwent dorsal nasal augmentation utilizing ATG and diced-cartilage grafts between 2005 and 2008, were included in the study. Although there was notable maintenance of the dorsal height, no complications occurred that required explantation over a mean follow-up of 15 months. The use of ATG to stabilize diced-cartilage grafts is a safe, reliable technique for dorsal nasal augmentation. The platelet gel provides growth factors while the fibrin glue creates a scaffold that allows stabilization and diffusion of nutrients to the cartilage graft.
Virtual patients: a critical literature review and proposed next steps.

The opposing forces of increased training expectations and reduced training resources have greatly impacted health professions education. Virtual patients (VPs), which take the form of interactive computer-based clinical scenarios, may help to reconcile this paradox.



We summarise research on VPs, highlight the spectrum of potential variation and identify an agenda for future research. We also critically consider the role of VPs in the educational armamentarium.



We propose that VPs' most unique and cost-effective function is to facilitate and assess the development of clinical reasoning. Clinical reasoning in experts involves a non-analytical process that matures through deliberate practice with multiple and varied clinical cases. Virtual patients are ideally suited to this task. Virtual patients can also be used in learner assessment, but scoring rubrics should emphasise non-analytical clinical reasoning rather than completeness of information or algorithmic approaches. Potential variations in VP design are practically limitless, yet few studies have rigorously explored design issues. More research is needed to inform instructional design and curricular integration.



Virtual patients should be designed and used to promote clinical reasoning skills. More research is needed to inform how to effectively use VPs.
A 77GHz CMOS VCO with 11.3GHz tuning range, 6dBm output power, and competitive phase noise in 65nm bulk CMOS

We demonstrate, using a foundry-based 65nm bulk technology, mmWave CMOS VCOs in the range of 38GHz and 77GHz with highest reported continuous tuning range (14%–25%), competitive phase noise (−88dBc/Hz at 1MHz offset at 77GHz), and high Pout (6dBm at 77GHz) needed to readily integrate with CMOS PA and to tolerate PVT variations. The DC power consumption of the 77GHz VCO is ∼190mW and that of the 38GHz VCOs is ∼60mW (∼16mW without buffer) from 1V supply (VDD). Device and design optimizations responsible for the high performance are presented. The impact of temperature and VDD variation (e.g., ∼2.6GHz shift in frequency and >5.3dB variation in Pout at 77GHz) is reported for the first time.
Broadband Tissue Mimicking Phantoms and a Patch Resonator for Evaluating Noninvasive Monitoring of Blood Glucose Levels

Tissue mimicking phantoms (TMPs) replicating the dielectric properties of wet skin, fat, blood, and muscle tissues for the 0.3 to 20 GHz frequency range are presented in this paper. The TMPs reflect the dielectric properties with maximum deviations of 7.7 units and 3.9 S/m for relative dielectric constant and conductivity, respectively, for the whole band. The dielectric properties of the blood mimicking material are further investigated by adding realistic glucose amounts and a Cole-Cole model used to compare the behavior with respect to changing glucose levels. In addition, a patch resonator was fabricated and tested with the four-layered physical phantom developed in house. It was observed that the input impedance of the resonator is sensitive to the changes in the dielectric properties and, hence, to the realistic glucose level changes in the blood layer.
Semi-Supervised Zero-Shot Classification with Label Representation Learning

Given the challenge of gathering labeled training data, zero-shot classification, which transfers information from observed classes to recognize unseen classes, has become increasingly popular in the computer vision community. Most existing zero-shot learning methods require a user to first provide a set of semantic visual attributes for each class as side information before applying a two-step prediction procedure that introduces an intermediate attribute prediction problem. In this paper, we propose a novel zero-shot classification approach that automatically learns label embeddings from the input data in a semi-supervised large-margin learning framework. The proposed framework jointly considers multi-class classification over all classes (observed and unseen) and tackles the target prediction problem directly without introducing intermediate prediction problems. It also has the capacity to incorporate semantic label information from different sources when available. To evaluate the proposed approach, we conduct experiments on standard zero-shot data sets. The empirical results show the proposed approach outperforms existing state-of-the-art zero-shot learning methods.
Polly – Polyhedral optimization in LLVM

Various powerful polyhedral techniques exist to optimize computation intensive programs effectively. Applying these techniques on any non-trivial program is still surprisingly difficult and often not as effective as expected. Most polyhedral tools are limited to a specific programming language. Even for this language, relevant code needs to match specific syntax that rarely appears in existing code. It is therefore hard or even impossible to process existing programs automatically. In addition, most tools target C or OpenCL code, which prevents effective communication with compiler internal optimizers. As a result target architecture specific optimizations are either little effective or not approached at all. In this paper we present Polly, a project to enable polyhedral optimizations in LLVM. Polly automatically detects and transforms relevant program parts in a language-independent and syntactically transparent way. Therefore, it supports programs written in most common programming languages and constructs like C++ iterators, goto based loops and pointer arithmetic. Internally it provides a state-of-the-art polyhedral library with full support for Z-polyhedra, advanced data dependency analysis and support for external optimizers. Polly includes integrated SIMD and OpenMP code generation. Through LLVM, machine code for CPUs and GPU accelerators, C source code and even hardware descriptions can be targeted.
Bilingual Correspondence Recursive Autoencoder for Statistical Machine Translation

Learning semantic representations and tree structures of bilingual phrases is beneficial for statistical machine translation. In this paper, we propose a new neural network model called Bilingual Correspondence Recursive Autoencoder (BCorrRAE) to model bilingual phrases in translation. We incorporate word alignments into BCorrRAE to allow it freely access bilingual constraints at different levels. BCorrRAE minimizes a joint objective on the combination of a recursive autoencoder reconstruction error, a structural alignment consistency error and a crosslingual reconstruction error so as to not only generate alignment-consistent phrase structures, but also capture different levels of semantic relations within bilingual phrases. In order to examine the effectiveness of BCorrRAE, we incorporate both semantic and structural similarity features built on bilingual phrase representations and tree structures learned by BCorrRAE into a state-of-the-art SMT system. Experiments on NIST Chinese-English test sets show that our model achieves a substantial improvement of up to 1.55 BLEU points over the baseline.
THE IMPORTANCE OF PAY IN EMPLOYEE MOTIVATION : DISCREPANCIES BETWEEN WHAT PEOPLE SAY AND WHAT THEY DO

Rynes, Colbert, and Brown (2002) presented the following statement to 959 members of the Society for Human Resource Management (SHRM): “Surveys that directly ask employees how important pay is to them are likely to overestimate pay’s true importance in actual decisions” (p. 158). If our interpretation (and that of Rynes et al.) of the research literature is accurate, then the correct true-false answer to the above statement is “false.” In other words, people are more likely to underreport than to overreport the importance of pay as a motivational factor in most situations. Put another way, research suggests that pay is much more important in people’s actual choices and behaviors than it is in their self-reports of what motivates them, much like the cartoon viewers mentioned in the quote above. Yet, only 35% of the respondents in the Rynes et al. study answered in a way consistent with research findings (i.e., chose “false”). Our objective in this article is to show that employee surveys regarding the importance of various factors in motivation generally produce results that are inconsistent with studies of actual employee behavior. In particular, we focus on well-documented findings that employees tend to say that pay THE IMPORTANCE OF PAY IN  DISCREPANCIES BETWEEN WHAT PEOPLE SAY AND WHAT THEY DO
Statistical analysis of motion patterns in AIS Data: Anomaly detection and motion prediction

The paper is devoted to statistical analysis of vessel motion patterns in the ports and waterways using AIS ship self-reporting data. From the real historic AIS data we extract motion patterns which are then used to construct the corresponding motion anomaly detectors. This is carried out in the framework of adaptive kernel density estimation. The anomaly detector is then sequentially applied to the real incoming AIS data for the purpose of anomaly detection. Under the null hypothesis (no anomaly), using the historic motion pattern data, we predict the motion of vessels using the Gaussian sum tracking filter.
Distributed Coordinate Descent for L1-regularized Logistic Regression

Solving logistic regression with L1-regularization in distributed settings is an important problem. This problem arises when training dataset is very large and cannot fit the memory of a single machine. We present d-GLMNET, a new algorithm solving logistic regression with L1-regularization in the distributed settings. We empirically show that it is superior over distributed online learning via truncated gradient.
Water Quality Monitoring and Control for Aquaculture Based on Wireless Sensor Networks

We have designed and presented a wireless sensor network monitoring and control system for aquaculture. The system can detect and control water quality parameters of temperature, dissolved oxygen content, pH value, and water level in real-time. The sensor nodes collect the water quality parameters and transmit them to the base station host computer through ZigBee wireless communication standard. The host computer is used for data analysis, processing and presentation using LabVIEW software platform. The water quality parameters will be sent to owners through short messages from the base station via the Global System for Mobile (GSM) module for notification. The experimental evaluation of the network performance metrics of quality of communication link, battery performance and data aggregation was presented. The experimental results show that the system has great prospect and can be used to operate in real world environment for optimum control of aquaculture
Single-Stage Power-Factor-Correction Circuit with Flyback Converter to Drive LEDs for Lighting Applications

White light emitting diode (LED) with high brightness has attracted a lot of attention from both industry and academia for its high efficiency, ease to drive, environmental friendliness, and long lifespan. They become possible applications to replace the incandescent bulbs and fluorescent lamps in residential, industrial and commercial lighting. The realization of this new lighting source requires both tight LED voltage regulation and high power factor as well. This paper proposed a single-stage flyback converter for the LED lighting applications and input power factor correction. A type-II compensator has been inserted in the voltage loop providing sufficient bandwidth and stable phase margin. The flyback converter is controlled with voltage mode pulse width modulation (PWM) and run in discontinuous conduction mode (DCM) so that the inductor current follows the rectified input voltage, resulting in high power factor. A prototype topology of closed-loop, single-stage flyback converter for LED driver circuit designed for an 18W LED lighting source is constructed and tested to verify the theoretical predictions. The measured performance of the LED lighting fixture can achieve a high power factor greater than 0.998 and a low total harmonic distortion less than 5.0%. Experimental results show the functionality of the overall system and prove it to be an effective solution for the new lighting applications.
Detection and tracking of boundary of unmarked roads

This paper presents a new method of detecting and tracking the boundaries of drivable regions in road without road-markings. As unmarked roads connect residential places to public roads, the capability of autonomously driving on such a roadway is important to truly realize self-driving cars in daily driving scenarios. To detect the left and right boundaries of drivable regions, our method first examines the image region at the front of ego-vehicle and then uses the appearance information of that region to identify the boundary of the drivable region from input images. Due to variation in the image acquisition condition, the image features necessary for boundary detection may not be present. When this happens, a boundary detection algorithm working frame-by-frame basis would fail to successfully detect the boundaries. To effectively handle these cases, our method tracks, using a Bayes filter, the detected boundaries over frames. Experiments using real-world videos show promising results.
Cloud Computing for parallel Scientific HPC Applications: Feasibility of Running Coupled Atmosphere-

In this article we describe the application of HPC standard benchmark tests to Amazon’s EC2 cloud computing system, in order to explore the utility of EC2 for modest HPC style applications. Based on these benchmarks we find that the EC2 cloud system is emerging as a credible solution for supporting responsive on-demand, small sized, HPC applications. We illustrate this point with a demonstration of a low-order coupled atmosphere-ocean simulation running in parallel on an EC2 system. This demonstration highlights one significant way in which cloud computing could impact traditional HPC computing paradigms that, together with related ease-of-use technologies (described only briefly in this paper), could shift the manner in which many HPC systems are deployed.
HPatches: A Benchmark and Evaluation of Handcrafted and Learned Local Descriptors

In this paper, we propose a novel benchmark for evaluating local image descriptors. We demonstrate that the existing datasets and evaluation protocols do not specify unambiguously all aspects of evaluation, leading to ambiguities and inconsistencies in results reported in the literature. Furthermore, these datasets are nearly saturated due to the recent improvements in local descriptors obtained by learning them from large annotated datasets. Therefore, we introduce a new large dataset suitable for training and testing modern descriptors, together with strictly defined evaluation protocols in several tasks such as matching, retrieval and classification. This allows for more realistic, and thus more reliable comparisons in different application scenarios. We evaluate the performance of several state-of-the-art descriptors and analyse their properties. We show that a simple normalisation of traditional hand-crafted descriptors can boost their performance to the level of deep learning based descriptors within a realistic benchmarks evaluation.
Identifying the Provision of Choices in Privacy Policy Text

Websites’ and mobile apps’ privacy policies, written in natural language, tend to be long and difficult to understand. Information privacy revolves around the fundamental principle of notice and choice, namely the idea that users should be able to make informed decisions about what information about them can be collected and how it can be used. Internet users want control over their privacy, but their choices are often hidden in long and convoluted privacy policy documents. Moreover, little (if any) prior work has been done to detect the provision of choices in text. We address this challenge of enabling user choice by automatically identifying and extracting pertinent choice language in privacy policies. In particular, we present a two-stage architecture of classification models to identify opt-out choices in privacy policy text, labelling common varieties of choices with a mean F1 score of 0.735. Our techniques enable the creation of systems to help Internet users to learn about their choices, thereby effectuating notice and choice and improving Internet privacy.
Privacy Regulations for Cloud Computing: Compliance and Implementation in Theory and Practice

Privacy is considered to be a fundamental human right (Movius and Krup, 2009). Around the world this has led to a large amount of legislation in the area of privacy. Nearly all national governments have imposed local privacy legislation. In the United States several states have imposed their own privacy legislation. In order to maintain a manageable scope this paper only addresses European Union wide and federal United States laws. In addition several US industry (self) regulations are also considered. Privacy regulations in emerging technologies are surrounded by uncertainty. This paper aims to clarify the uncertainty relating to privacy regulations with respect to Cloud Computing and to identify the main open issues that need to be addressed for further research. This paper is based on existing literature and a series of interviews and questionnaires with various Cloud Service Providers (CSPs) that have been performed for the first author’s MSc thesis (Ruiter, 2009). The interviews and questionnaires resulted in data on privacy and security procedures from ten CSPs and while this number is by no means large enough to make any definite conclusions the results are, in our opinion, interesting enough to publish in this paper. The remainder of the paper is organized as follows: the next section gives some basic background on Cloud Computing. Section 3 provides
A demonstration of the MaxStream federated stream processing system

MaxStream is a federated stream processing system that seamlessly integrates multiple autonomous and heterogeneous Stream Processing Engines (SPEs) and databases. In this paper, we propose to demonstrate the key features of MaxStream using two application scenarios, namely the Sales Map & Spikes business monitoring scenario and the Linear Road Benchmark, each with a different set of requirements. More specifically, we will show how the MaxStream Federator can translate and forward the application queries to two different commercial SPEs (Coral8 and StreamBase), as well as how it does so under various persistency requirements.