nils-herrmann/clean_scirepeval huggingface dataset:
Creativity among entrepreneurship students : comparing engineering and business education

As creativity is increasingly recognised as a vital component of entrepreneurship, researchers and educators struggle to reform enterprise pedagogy. To help in this effort, we use a personality test and open-ended interviews to explore creativity between two groups of entrepreneurship masters’ students: one at a business school and one at an engineering school. The findings indicate that both groups had high creative potential, but that engineering students channelled this into practical and incremental efforts whereas the business students were more speculative and had a clearer market focus. The findings are drawn on to make some suggestions for entrepreneurship education.
Stabilizing periodontally compromised teeth with glass fiber-reinforced composite resin – case report

The tooth mobility due to periodontal bone loss can cause masticatory discomfort, mainly in protrusive movements in the region of the mandibular anterior teeth. Thus, the splinting is a viable alternative to keep them in function satisfactorily.  This study aimed to demonstrate, through a clinical case with medium-term following-up, the clinical application of splinting with glass fiber-reinforced composite resin.  Female patient, 73 years old, complained about masticatory discomfort related to the right mandibular lateral incisor. Clinical and radiographic evaluation showed grade 2 dental mobility, bone loss and increased periodontal ligament space. The proposed treatment was splinting with glass fiber-reinforced composite resin from the right mandibular canine to left mandibular canine.  Four-year follow-up showed favorable clinical and radiographic results with respect to periodontal health and maintenance of functional aspects.  The splinting with glass fiber-reinforced composite resin is a viable technique and stable over time for the treatment of tooth mobility.
Privacy-Preserving Deep Inference for Rich User Data on The Cloud

Deep neural networks are increasingly being used in a variety of machine learning applications applied to rich user data on the cloud. However, this approach introduces a number of privacy and efficiency challenges, as the cloud operator can perform secondary inferences on the available data. Recently, advances in edge processing have paved the way for more efficient, and private, data processing at the source for simple tasks and lighter models, though they remain a challenge for larger, and more complicated models. In this paper, we present a hybrid approach for breaking down large, complex deep models for cooperative, privacy-preserving analytics. We do this by breaking down the popular deep architectures and fine-tune them in a particular way. We then evaluate the privacy benefits of this approach based on the information exposed to the cloud service. We also asses the local inference cost of different layers on a modern handset for mobile applications. Our evaluations show that by using certain kind of fine-tuning and embedding techniques and at a small processing costs, we can greatly reduce the level of information available to unintended tasks applied to the data feature on the cloud, and hence achieving the desired tradeoff between privacy and performance.
Compact SIW 3×3 butler matrix for 5G mobile devices

This paper presents a compact SIW (substrate integrated waveguide) 3×3 Butler matrix (BM) for 5G mobile applications. The detailed structuring procedures, parameter determinations of each involved component are provided. To validate the 3×3 BM, a slot array is designed. The cascading simulations and prototype measurements are also carried out. The overall performance and dimension show that it can be used for 5G mobile devices. The measured S-parameters agree well with the simulated ones. The measured gains are in the range of 8.1 dBi ∼ 11.1 dBi, 7.1 dBi ∼ 9.8 dBi and 8.9 dBi ∼ 11 dBi for port 1∼3 excitations.
Type Classes with Existential Types

We argue that the novel combination of type classes and existential types in a single language yields significant expressive power. We explore this combination in the context of higher-order functional languages with static typing, parametric polymorphism, algebraic data types, and Hindley-Milner type inference. Adding existential types to an existing functional language that already features type classes requires only a minor syntactic extension. We first demonstrate how to provide existential quantification over type classes by extending the syntax of algebraic data type definitions and give examples of possible uses. We then develop a type system and a type inference algorithm for the resulting language. Finally, we present a formal semantics by translation to an implicitly-typed second-order λ-calculus and show that the type system is semantically sound. Our extension has been implemented in the Chalmers Haskell B. system, and all examples from this paper have been developed using this system.
Compositional GAN: Learning Conditional Image Composition

Generative Adversarial Networks (GANs) can produce images of surprising complexity and realism, but are generally modeled to sample from a single latent source ignoring the explicit spatial interaction between multiple entities that could be present in a scene. Capturing such complex interactions between different objects in the world, including their relative scaling, spatial layout, occlusion, or viewpoint transformation is a challenging problem. In this work, we propose to model object composition in a GAN framework as a self-consistent composition-decomposition network. Our model is conditioned on the object images from their marginal distributions to generate a realistic image from their joint distribution by explicitly learning the possible interactions. We evaluate our model through qualitative experiments and user evaluations in both the scenarios when either paired or unpaired examples for the individual object images and the joint scenes are given during training. Our results reveal that the learned model captures potential interactions between the two object domains given as input to output new instances of composed scene at test time in a reasonable fashion.
Study of Various Methods for Brain Tumour Segmentation from MRI Images

Segmentation means segregating area of interest from the image. The aim of image segmentation is to cluster the pixels into salient image regions i.e. regions corresponding to individual surfaces, objects, or natural parts of objects. Automatic Brain tumour segmentation is a sensitive step in medical field. A significant medical informatics task is to perform the indexing of the patient databases according to image location, size and other characteristics of brain tumours based on magnetic resonance (MR) imagery. This requires segmenting tumours from different MR imaging modalities. Automated brain tumour segmentation from MR modalities is a challenging, computationally intensive task.Image segmentation plays an important role in image processing. MRI is generally more useful for brain tumour detection because it provides more detailed information about its type, position and size. For this reason, MRI imaging is the choice of study for the diagnostic purpose and, thereafter, for surgery and monitoring treatment outcomes. This paper presents a review of the various methods used in brain MRI image segmentation. The review covers imaging modalities, magnetic resonance imaging and methods for segmentation approaches. The paper concludes with a discussion on the upcoming trend of advanced researches in brain image segmentation. Region growing, Level set method, Split and merge algorithm, MRI images
A Synthesis Methodology for Ternary Logic Circuits in Emerging Device Technologies

Automatic synthesis of digital circuits has played a key role in obtaining high-performance designs. While considerable work has been done in the past, emerging device technologies call for a need to re-examine the synthesis approaches, so that better circuits that harness the true power of these technologies can be developed. This paper presents a methodology for synthesis applicable to devices that support ternary logic. We present an algorithm for synthesis that combines a geometrical representation with unary operators of multivalued logic. The geometric representation facilitates scanning appropriately to obtain simple sum-of-products expressions in terms of unary operators. An implementation based on Python is described. The power of the approach lies in its applicability to a wide variety of circuits. The proposed approach leads to the savings of 26% and 22% in transistor-count, respectively, for a ternary full-adder and a ternary content-addressable memory (TCAM) over the best existing designs. Furthermore, the proposed approach requires, on an average, less than 10% of the number of the transistors in comparison with a recent decoder-based design for various ternary benchmark circuits. Extensive HSPICE simulation results show roughly 92% reduction in power-delay product (PDP) for a  $12\times 12$  TCAM and 60% reduction in PDP for a 24-ternary digit barrel shifter over recent designs.
What ’ s New About Cloud Computing Security ?

While the economic case for cloud computing is compelling, the security challenges it poses are equally striking. In this work we strive to frame the full space of cloud-computing security issues, attempting to separate justified concerns from possible over-reactions. We examine contemporary and historical perspectives from industry, academia, government, and “black hats”. We argue that few cloud computing security issues are fundamentally new or fundamentally intractable; often what appears “new” is so only relative to “traditional” computing of the past several years. Looking back further to the time-sharing era, many of these problems already received attention. On the other hand, we argue that two facets are to some degree new and fundamental to cloud computing: the complexities of multi-party trust considerations, and the ensuing need for mutual auditability.
Harnessing Twitter "Big Data" for Automatic Emotion Identification

User generated content on Twitter (produced at an enormous rate of 340 million tweets per day) provides a rich source for gleaning people's emotions, which is necessary for deeper understanding of people's behaviors and actions. Extant studies on emotion identification lack comprehensive coverage of "emotional situations" because they use relatively small training datasets. To overcome this bottleneck, we have automatically created a large emotion-labeled dataset (of about 2.5 million tweets) by harnessing emotion-related hash tags available in the tweets. We have applied two different machine learning algorithms for emotion identification, to study the effectiveness of various feature combinations as well as the effect of the size of the training data on the emotion identification task. Our experiments demonstrate that a combination of unigrams, big rams, sentiment/emotion-bearing words, and parts-of-speech information is most effective for gleaning emotions. The highest accuracy (65.57%) is achieved with a training data containing about 2 million tweets.
Generalizing Over Uncertain Dynamics for Online Trajectory Generation

We present an algorithm which learns an online trajectory generator that can generalize over varying and uncertain dynamics. When the dynamics is certain, the algorithm generalizes across model parameters. When the dynamics is partially observable, the algorithm generalizes across different observations. To do this, we employ recent advances in supervised imitation learning to learn a trajectory generator from a set of example trajectories computed by a trajectory optimizer. In experiments in two simulated domains, it finds solutions that are nearly as good as, and sometimes better than, those obtained by calling the trajectory optimizer on line. The online execution time is dramatically decreased, and the off-line training time is reasonable.
Biodiversity in tropical agroforests and the ecological role of ants and ant diversity in predatory function

1. Intensive agricultural practices drive biodiversity loss with potentially drastic consequences for ecosystem services. To advance conservation and production goals, agricultural practices should be compatible with biodiversity. Traditional or less intensive systems (i.e. with fewer agrochemicals, less mechanisation, more crop species) such as shaded coffee and cacao agroforests are highlighted for their ability to provide a refuge for biodiversity and may also enhance certain ecosystem functions (i.e. predation). 2. Ants are an important predator group in tropical agroforestry systems. Generally, ant biodiversity declines with coffee and cacao intensification yet the literature lacks a summary of the known mechanisms for ant declines and how this diversity loss may affect the role of ants as predators. 3. Here, how shaded coffee and cacao agroforestry systems protect biodiversity and may preserve related ecosystem functions is discussed in the context of ants as predators. Specifically, the relationships between biodiversity and predation, links between agriculture and conservation, patterns and mechanisms for ant diversity loss with agricultural intensification, importance of ants as control agents of pests and fungal diseases, and whether ant diversity may influence the functional role of ants as predators are addressed. Furthermore, because of the importance of homopteran-tending by ants in the ecological and agricultural literature, as well as to the success of ants as predators, the costs and benefits of promoting ants in agroforests are discussed. 4. Especially where the diversity of ants and other predators is high, as in traditional agroforestry systems, both agroecosystem function and conservation goals will be advanced by biodiversity protection.
A Simple, Fast Diverse Decoding Algorithm for Neural Generation

In this paper, we propose a simple, fast decoding algorithm that fosters diversity in neural generation. The algorithm modifies the standard beam search algorithm by adding an intersibling ranking penalty, favoring choosing hypotheses from diverse parents. We evaluate the proposed model on the tasks of dialogue response generation, abstractive summarization and machine translation. We find that diverse decoding helps across all tasks, especially those for which reranking is needed. We further propose a variation that is capable of automatically adjusting its diversity decoding rates for different inputs using reinforcement learning (RL). We observe a further performance boost from this RL
Endogenous Federal Grants and Crowd-out of State Government Spending : Theory and Evidence from the Federal Highway Aid Program

Contrary to simple theoretical predictions, existing evidence suggests that federal grants do not crowd out state government spending. A legislative bargaining model with endogenous grants documents a positive correlation between grant receipts and preferences for public goods; this correlation has likely biased existing work against measuring crowd-out. To correct for such endogeneity, the model motivates instruments based on the political power of state congressional delegations. Exploiting this exogenous variation in grants, the instrumental variables estimator reports crowd-out that is statistically and economically signi cant. This endogeneity may explain the  ypaper effect, a nonequivalence between grant receipts and private income.
An Overview of Diode Pumped Solid State ( DPSS ) Lasers

The primary purpose of text is to explain the mutual influences between the physical and dynamic processes in solids and their lasing properties. It begins with a brief overview of historical developments and the basics of solid state laser materials and design, then describes optical dynamics and energy transfer in solids and analyzes the properties of rare earth and transition metal ion doped crystals, glasses and ceramics. Diode laser pumps for solid state lasers are reviewed with special emphasis on current and future capabilities. The text also outlines the factors that influence the development of new laser materials, and reports on recent developments and technological advances in solid state lasers and photonics that use novel materials and techniques. Included are discussions of pumps for continuous wave or pulsed and side pumping configurations. The use of diode lasers instead of flashlamps as optical pump sources for solid state lasers offers significant advantages such as higher efficiency and longer lifetime. The potential applications of this technology to device singulation for electronic and power generation devices will be described.
Predicting Complete 3D Models of Indoor Scenes

One major goal of vision is to infer physical models of objects, surfaces, and their layout from sensors. In this paper, we aim to interpret indoor scenes from one RGBD image. Our representation encodes the layout of walls, which must conform to a Manhattan structure but is otherwise flexible, and the layout and extent of objects, modeled with CAD-like 3D shapes. We represent both the visible and occluded portions of the scene, producing a complete 3D parse. Such a scene interpretation is useful for robotics and visual reasoning, but difficult to produce due to the wellknown challenge of segmentation, the high degree of occlusion, and the diversity of objects in indoor scene. We take a data-driven approach, generating sets of potential object regions, matching to regions in training images, and transferring and aligning associated 3D models while encouraging fit to observations and overall consistency. We demonstrate encouraging results on the NYU v2 dataset and highlight a variety of interesting directions for future work.
Time Series Forecasting using RNNs: an Extended Attention Mechanism to Model Periods and Handle Missing Values

In this paper, we study the use of recurrent neural networks (RNNs) for modeling and forecasting time series. We first illustrate the fact that standard sequence-to-sequence RNNs neither capture well periods in time series nor handle well missing values, even though many real life times series are periodic and contain missing values. We then propose an extended attention mechanism that can be deployed on top of any RNN and that is designed to capture periods and make the RNN more robust to missing values. We show the effectiveness of this novel model through extensive experiments with multiple univariate and multivariate datasets.
Why do people use information technology? A critical review of the technology acceptance model

Information systems (IS) implementation is costly and has a relatively low success rate. Since the seventies, IS research has contributed to a better understanding of this process and its outcomes. The early efforts concentrated on the identification of factors that facilitated IS use. This produced a long list of items that proved to be of little practical value. It became obvious that, for practical reasons, the factors had to be grouped into a model in a way that would facilitate analysis of IS use. In 1985, Fred Davis suggested the technology acceptance model (TAM). It examines the mediating role of perceived ease of use and perceived usefulness in their relation between systems characteristics (external variables) and the probability of system use (an indicator of system success). More recently, Davis proposed a new version of his model: TAM2. It includes subjective norms, and was tested with longitudinal research designs. Overall the two explain about 40% of system’s use. Analysis of empirical research using TAM shows that results are not totally consistent or clear. This suggests that significant factors are not included in the models. We conclude that TAM is a useful model, but has to be integrated into a broader one which would include variables related to both human and social change processes, and to the adoption of the innovation model.
CMU / SEI-92-TR-0 12 ESC-TR-92-012 Issues in Requirements Elicitation

There are many problems associated with requirements engineering, including problems in defining the system scope, problems in fostering understanding among the different communities affected by the development of a given system, and problems in dealing with the volatile nature of requirements. These problems may lead to poor requirements and the cancellation of system development, or else the development of a system that is later judged unsatisfactory or unacceptable, has high maintenance costs, or undergoes frequent changes. By improving requirements elicitation, the requirements engineering process can be improved, resulting in enhanced system requirements and potentially a much better system. Requirements engineering can be decomposed into the activities of requirements elicitation, specification, and validation. Most of the requirements techniques and tools today focus on specification, i.e., the representation of the requirements. This report concentrates instead on elicitation concerns, those problems with requirements engineering that are not adequately addressed by specification techniques. An elicitation methodology is proposed to handle these concerns. This new elicitation methodology strives to incorporate the advantages of existing elicitation techniques while comprehensively addressing the activities performed during requirements elicitation. These activities include fact-finding, requirements gathering, evaluation and rationalization, prioritization, and integration. Taken by themselves, existing elicitation techniques are lacking in one or more of these areas.
The effect of surroundings on gaming experience

Today, digital games are available on a variety of mobile devices, such as tablet devices, portable game consoles and smart phones. Not only that, the latest mixed reality technology on mobile devices allows mobile games to integrate the real world environment into gameplay. However, little has been done to test whether the surroundings of play influence gaming experience. In this paper, we describe two studies done to test the effect of surroundings on immersion.  One uses mixed reality games to investigate whether the integration of the real world environment reduces engagement. Whereas Study Two explored the effects of manipulating the lighting level, and therefore reducing visibility, of the surroundings. We found that immersion is reduced in the conditions where visibility of the surroundings is high. We argue that higher awareness of the surroundings has a strong impact on gaming experience.
Gesture-Driven System for Intelligent Building Control

In recent years, intelligent building systems have become standard in residential buildings as well as commercial buildings. This chapter presents a novel system for controlling such buildings using natural user interfaces. Due to advanced logic, it is capable of identifying people occupying defined areas and recognizing their gestures. The system cooperates with a Domatiq System – Intelligent Building Management System, to provide an overall control of the building. This allows the user to control particular building components in a more natural way. Results shows that for the elderly or disabled people a more natural method of controlling some appliances is more convenient than, for example, by remote controllers. This system can also be used to control and monitor such people, giving them a sense of comfort and safety in their homes while respecting their privacy.