nils-herrmann/clean_scirepeval huggingface dataset:
Public-Private Partnerships and the 2030 Agenda for Sustainable Development : Fit for purpose ? 1

1 Jomo was an Assistant Secretary General in the United Nations system responsible for economic research during 2005-2015.; Chowdhury (Chief, Multi-Stakeholder Engagement & Outreach, Financing for Development Office, UN-DESA); Sharma (Senior Economic Affairs Officer, Financing for Development Office, UN-DESA); Platz (Economic Affairs Officer, Financing for Development Office, UN-DESA); corresponding author: Anis Chowdhury (chowdhury4@un.org; anis.z.chowdhury@gmail.com). Thanks to colleagues at the Financing for Development Office of UN-DESA and an anonymous referee for their helpful comments. Thanks also to Alexander Kucharski for his excellent support in gathering data and producing figure charts and to Jie Wei for drawing the flow charts. However, the usual caveats apply.
Analysis and design of a dual-rotor axial-flux vernier permanent magnet machine

This paper proposes a dual-rotor, toroidal-winding, axial-flux vernier permanent magnet (VPM) machine. By the combination of toroidal windings with the rotor-stator-rotor topology, the end winding length of the machine is significantly reduced when compared with the regular VPM machine. Based on the airgap permeance function, the back-EMF and torque expressions are derived, through which the nature of this machine is revealed. The influence of pole ratio (ratio of rotor pole pair number to stator pole pair number) and main geometric parameters such as slot opening, magnet thickness etc., on torque performance is then analytically investigated. Both the quasi-3-dimensional (quasi-3D) finite element analysis (FEA) and 3D FEA are applied to verify the theoretical analysis. With the current density of 4.2 A/mm2, the torque density of the proposed machine can reach 32.6 kNm/m3. A prototype has been designed and is in manufacturing process. Experimental validation will be presented in the future.
Orthomode Transducers With Folded Double-Symmetry Junctions for Broadband and Compact Antenna Feeds

Orthomode transducers (OMTs) using folded six-port waveguide junctions are presented for obtaining the maximum compactness in antenna feeds with very wide performance. The proposed junctions are based on the well-known turnstile and Bøifot configurations, having two symmetry planes for keeping the isolation between orthogonal polarizations and the control of higher order modes. The folded arms provide a combined effect: good matching with very significant size reduction, especially in the transversal plane, reducing mass in satellite systems and allowing feeding eventual dense horn antenna arrays. Two Ku-band OMTs are presented in order to illustrate the advantages of the introduced junctions. The first design covers the band 10.4–18.8 GHz (57.5%) with 24 dB return loss. The second OMT, for the 12.60–18.25 GHz band, has measured return loss better than 29 dB in the design band and insertion loss smaller than 0.11 dB for both polarizations.
Enhancing digital libraries with TechLens

The number of research papers available is growing at a staggering rate. Researchers need tools to help them find the papers they should read among all the papers published each year. In this paper, we present and experiment with hybrid recommender algorithms that combine Collaborative Filtering and Content-based. Filtering to recommend research papers to users. Our hybrid algorithms combine the strengths of each filtering approach to address their individual weaknesses. We evaluated our algorithms through offline experiments on a database of 102, 000 research papers, and through an online experiment with 110 users. For both experiments we used a dataset created from the CiteSeer repository of computer science research papers. We developed separate English and Portuguese versions of the interface and specifically recruited American and Brazilian users to test for cross-cultural effects. Our results show that users value paper recommendations, that the hybrid algorithms can be successfully combined, that different algorithms are more suitable for recommending different kinds of papers, and that users with different levels of experience perceive recommendations differently These results can be applied to develop recommender systems for other types of digital libraries.
A Design Science Research Methodology for Information Systems Research

The paper motivates, presents, demonstrates in use, and evaluates a methodology for conducting design science (DS) research in information systems. DS is of importance in a discipline oriented to the creation of successful artifacts. Several IS researchers have pioneered DS research in IS, yet over the last 15 years little DS research has been done within the discipline. The lack of a methodology to serve as a commonly accepted framework for DS research and of a template for its presentation may have contributed to its slow adoption. The design science research methodology (DSRM) presented here incorporates principles, practices, and procedures required to carry out such research and meets three objectives: it is consistent with prior literature, it provides a nominal process model for doing DS research, and it provides a mental model for presenting and evaluating DS research in IS. The DS process includes six steps: problem identification and motivation, definition of the objectives for a solution, design and development, demonstration, evaluation, and communication. We demonstrate and evaluate the methodology by presenting four case studies in terms of the DSRM, including cases that present the design of a database to support health assessment methods, a software reuse measure, an Internet video telephony application, and an IS planning method. The designed methodology effectively satisfies the three objectives and has the potential to help aid the acceptance of DS research in the IS discipline.
English in South Africa

Recent estimates based on the 1991 census (Schuring 1993) indicate that approximately 45 per cent of the South African population have a speaking knowledge of English (the majority of the population speaking an African language, such as Zulu, Xhosa, Tswana, or Venda, as home language). The number of individuals who cite English as a home language appears to be, however, only about 10 per cent of the population. Of this figure it would seem that at least one in three English-speakers come from ethnic groups other than the white one (in proportionally descending order, from the South African Indian, Coloured, and Black ethnic groups). This figure has shown some increase in recent years.
Unikernel Monitors: Extending Minimalism Outside of the Box

Recently, unikernels have emerged as an exploration of minimalist software stacks to improve the security of applications in the cloud. In this paper, we propose extending the notion of minimalism beyond an individual virtual machine to include the underlying monitor and the interface it exposes. We propose unikernel monitors . Each unikernel is bundled with a tiny, specialized monitor that only contains what the unikernel needs both in terms of interface and implementation. Unikernel monitors improve isolation through minimal interfaces, reduce complexity, and boot unikernels quickly. Our initial prototype,ukvm, is less than 5% the code size of a traditional monitor, and boots MirageOS unikernels in as little as 10ms (8× faster than a traditional monitor).
Spectrally-normalized margin bounds for neural networks

This paper presents a margin-based multiclass generalization bound for neural networks that scales with their margin-normalized spectral complexity: their Lipschitz constant, meaning the product of the spectral norms of the weight matrices, times a certain correction factor. This bound is empirically investigated for a standard AlexNet network trained with SGD on the mnist and cifar10 datasets, with both original and random labels; the bound, the Lipschitz constants, and the excess risks are all in direct correlation, suggesting both that SGD selects predictors whose complexity scales with the difficulty of the learning task, and secondly that the presented bound is sensitive to this complexity.
How Transferable Are CNN-Based Features for Age and Gender Classification?

Age and gender are complementary soft biometric traits for face recognition. Successful estimation of age and gender from facial images taken under real-world conditions can contribute improving the identification results in the wild. In this study, in order to achieve robust age and gender classification in the wild, we have benefited from Deep Convolutional Neural Networks based representation. We have explored transferability of existing deep convolutional neural network (CNN) models for age and gender classification. The generic AlexNet-like architecture and domain specific VGG-Face CNN model are employed and fine-tuned with the Adience dataset prepared for age and gender classification in uncontrolled environments. In addition, task specific GilNet CNN model has also been utilized and used as a baseline method in order to compare with transferred models. Experimental results show that both transferred deep CNN models outperform the GilNet CNN model, which is the state-of-the-art age and gender classification approach on the Adience dataset, by an absolute increase of 7% and 4.5% in accuracy, respectively. This outcome indicates that transferring a deep CNN model can provide better classification performance than a task specific CNN model, which has a limited number of layers and trained from scratch using a limited amount of data as in the case of GilNet. Domain specific VGG-Face CNN model has been found to be more useful and provided better performance for both age and gender classification tasks, when compared with generic AlexNet-like model, which shows that transfering from a closer domain is more useful.
Clinical Endocannabinoid Deficiency Reconsidered: Current Research Supports the Theory in Migraine, Fibromyalgia, Irritable Bowel, and Other Treatment-Resistant Syndromes

Medicine continues to struggle in its approaches to numerous common subjective pain syndromes that lack objective signs and remain treatment resistant. Foremost among these are migraine, fibromyalgia, and irritable bowel syndrome, disorders that may overlap in their affected populations and whose sufferers have all endured the stigma of a psychosomatic label, as well as the failure of endless pharmacotherapeutic interventions with substandard benefit. The commonality in symptomatology in these conditions displaying hyperalgesia and central sensitization with possible common underlying pathophysiology suggests that a clinical endocannabinoid deficiency might characterize their origin. Its base hypothesis is that all humans have an underlying endocannabinoid tone that is a reflection of levels of the endocannabinoids, anandamide (arachidonylethanolamide), and 2-arachidonoylglycerol, their production, metabolism, and the relative abundance and state of cannabinoid receptors. Its theory is that in certain conditions, whether congenital or acquired, endocannabinoid tone becomes deficient and productive of pathophysiological syndromes. When first proposed in 2001 and subsequently, this theory was based on genetic overlap and comorbidity, patterns of symptomatology that could be mediated by the endocannabinoid system (ECS), and the fact that exogenous cannabinoid treatment frequently provided symptomatic benefit. However, objective proof and formal clinical trial data were lacking. Currently, however, statistically significant differences in cerebrospinal fluid anandamide levels have been documented in migraineurs, and advanced imaging studies have demonstrated ECS hypofunction in post-traumatic stress disorder. Additional studies have provided a firmer foundation for the theory, while clinical data have also produced evidence for decreased pain, improved sleep, and other benefits to cannabinoid treatment and adjunctive lifestyle approaches affecting the ECS.
Constructing an Index for Brand Equity : A Hospital Example

If two hospitals are providing identical services in all respects, except for the brand name, why are customers willing to pay more for one hospital than the other? That is, the brand name is not just a name, but a name that contains value (brand equity). Brand equity is the value that the brand name endows to the product, such that consumers are willing to pay a premium price for products with the particular brand name. Accordingly, a company needs to manage its brand carefully so that its brand equity does not depreciate. Although measuring brand equity is important, managers have no brand equity index that is psychometrically robust and parsimonious enough for practice. Indeed, index construction is quite different from conventional scale development. Moreover, researchers might still be unaware of the potential appropriateness of formative indicators for operationalizing particular constructs. Toward this end, drawing on the brand equity literature and following the index construction procedure, this study creates a brand equity index for a hospital. The results reveal a parsimonious five-indicator brand equity index that can adequately capture the full domain of brand equity. This study also illustrates the differences between index construction and scale development.
Stateful Scalable Stream Processing at LinkedIn

Distributed stream processing systems need to support stateful processing, recover quickly from failures to resume such processing, and reprocess an entire data stream quickly. We present Apache Samza, a distributed system for stateful and fault-tolerant stream processing. Samza utilizes a partitioned local state along with a low-overhead background changelog mechanism, allowing it to scale to massive state sizes (hundreds of TB) per application. Recovery from failures is sped up by re-scheduling based on Host Affinity. In addition to processing infinite streams of events, Samza supports processing a finite dataset as a stream, from either a streaming source (e.g., Kafka), a database snapshot (e.g., Databus), or a file system (e.g. HDFS), without having to change the application code (unlike the popular Lambdabased architectures which necessitate maintenance of separate code bases for batch and stream path processing). Samza is currently in use at LinkedIn by hundreds of production applications with more than 10, 000 containers. Samza is an open-source Apache project adopted by many top-tier companies (e.g., LinkedIn, Uber, Netflix, TripAdvisor, etc.). Our experiments show that Samza: a) handles state efficiently, improving latency and throughput by more than 100× compared to using a remote storage; b) provides recovery time independent of state size; c) scales performance linearly with number of containers; and d) supports reprocessing of the data stream quickly and with minimal interference on real-time traffic.
Gauging Similarity with n-Grams: Language-Independent Categorization of Text.

A language-independent means of gauging topical similarity in unrestricted text is described. The method combines information derived from n-grams (consecutive sequences of n characters) with a simple vector-space technique that makes sorting, categorization, and retrieval feasible in a large multilingual collection of documents. No prior information about document content or language is required. Context, as it applies to document similarity, can be accommodated by a well-defined procedure. When an existing document is used as an exemplar, the completeness and accuracy with which topically related documents are retrieved is comparable to that of the best existing systems. The results of a formal evaluation are discussed, and examples are given using documents in English and Japanese.
A cross-cultural examination of presenteeism and supervisory support

The aim of this study was two-fold: first, to examine the noxious effects of presenteeism on employees’ work well-being in a cross-cultural context involving Chinese and British employees; second, to explore the role of supervisory support as a pan-cultural stress buffer in the presenteeism process.  Using structured questionnaires, the authors compared data collected from samples of 245 Chinese and 128 British employees working in various organizations and industries.  Cross-cultural comparison revealed that the act of presenteeism was more prevalent among Chinese and they reported higher levels of strains than their British counterparts. Hierarchical regression analyses showed that presenteeism had noxious effects on exhaustion for both Chinese and British employees. Moreover, supervisory support buffered the negative impact of presenteeism on exhaustion for both Chinese and British employees. Specifically, the negative relation between presenteeism and exhaustion was stronger for those with more supervisory support.  Presenteeism may be used as a career-protecting or career-promoting tactic. However, the negative effects of this behavior on employees’ work well-being across the culture divide should alert us to re-think its pros and cons as a career behavior. Employees in certain cultures (e.g. the hardworking Chinese) may exhibit more presenteeism behaviour, thus are in greater risk of ill-health.  This is the first cross-cultural study demonstrating the universality of the act of presenteeism and its damaging effects on employees’ well-being. The authors’ findings of the buffering role of supervisory support across cultural contexts highlight the necessity to incorporate resources in mitigating the harmful impact of presenteeism.
Advancing the field of 3 D biomaterial printing

3D biomaterial printing has emerged as a potentially revolutionary technology, promising to transform both research and medical therapeutics. Although there has been recent progress in the field, on-demand fabrication of functional and transplantable tissues and organs is still a distant reality. To advance to this point, there are two major technical challenges that must be overcome. The first is expanding upon the limited variety of available 3D printable biomaterials (biomaterial inks), which currently do not adequately represent the physical, chemical, and biological complexity and diversity of tissues and organs within the human body. Newly developed biomaterial inks and the resulting 3D printed constructs must meet numerous interdependent requirements, including those that lead to optimal printing, structural, and biological outcomes. The second challenge is developing and implementing comprehensive biomaterial ink and printed structure characterization combined with in vitro and in vivo tissueand organ-specific evaluation. This perspective outlines considerations for addressing these technical hurdles that, once overcome, will facilitate rapid advancement of 3D biomaterial printing as an indispensable tool for both investigating complex tissue and organ morphogenesis and for developing functional devices for a variety of diagnostic and regenerative medicine applications. PAPER 5 Contributed equally to this work.
Promoting Positive Technological Development in a Kindergarten Makerspace : A Qualitative Case Study

As education communities grow more interested in STEM (science, technology, engineering, and mathematics), schools have integrated more technology and engineering opportunities into their curricula. Makerspaces for all ages have emerged as a way to support STEM learning through creativity, community building, and hands-on learning. However, little research has evaluated the learning that happens in these spaces, especially in young children. One framework that has been used successfully as an evaluative tool in informal and technology-rich learning spaces is Positive Technological Development (PTD). PTD is an educational framework that describes positive behaviors children exhibit while engaging in digital learning experiences. In this exploratory case study, researchers observed children in a makerspace to determine whether the environment (the space and teachers) contributed to children’s Positive Technological Development. N = 20 children and teachers from a Kindergarten classroom were observed over 6 hours as they engaged in makerspace activities. The children’s activity, teacher’s facilitation, and the physical space were evaluated for alignment with the PTD framework. Results reveal that children showed high overall PTD engagement, and that teachers and the space supported children’s learning in complementary aspects of PTD. Recommendations for practitioners hoping to design and implement a young children’s makerspace are discussed.
Few-Shot Learning Through an Information Retrieval Lens

Few-shot learning refers to understanding new concepts from only a few examples. We propose an information retrieval-inspired approach for this problem that is motivated by the increased importance of maximally leveraging all the available information in this low-data regime. We define a training objective that aims to extract as much information as possible from each training batch by effectively optimizing over all relative orderings of the batch points simultaneously. In particular, we view each batch point as a ‘query’ that ranks the remaining ones based on its predicted relevance to them and we define a model within the framework of structured prediction to optimize mean Average Precision over these rankings. Our method achieves impressive results on the standard few-shot classification benchmarks while is also capable of few-shot retrieval.
ORB: An efficient alternative to SIFT or SURF

Feature matching is at the base of many computer vision problems, such as object recognition or structure from motion. Current methods rely on costly descriptors for detection and matching. In this paper, we propose a very fast binary descriptor based on BRIEF, called ORB, which is rotation invariant and resistant to noise. We demonstrate through experiments how ORB is at two orders of magnitude faster than SIFT, while performing as well in many situations. The efficiency is tested on several real-world applications, including object detection and patch-tracking on a smart phone.