nils-herrmann/clean_scirepeval huggingface dataset:
DESIGN OF BPF USING INTERDIGITAL BANDPASS FILTER ON CENTER FREQUENCY 3 GHZ .

In this paper a high performance Bandpass micro strip filter is presented. The filter is designed using interdigital bandpass filter to improve the performance of Bandpass filter. The designed filter is based on 5th order chebyshev low pass prototype with .1 dB passband ripples. The filter operates with passband from 2GHz-4 GHz, produces a fractional bandwidth of 66%. The filter is designed on a FR4 substrate with relative dielectric constant of 4.4 and a thickness of 1.57mm. The simulated result using HFSS shows an insertion loss (S21) less than -1.8dB and return loss (S11) better than -8 dB.
Duplicating road patterns in south african informal settlements using procedural techniques

The formation of informal settlements in and around urban complexes has largely been ignored in the context of procedural city modeling. However, many cities in South Africa and globally can attest to the presence of such settlements. This paper analyses the phenomenon of informal settlements from a procedural modeling perspective. Aerial photography from two South African urban complexes, namely Johannesburg and Cape Town is used as a basis for the extraction of various features that distinguish different types of settlements. In particular, the road patterns which have formed within such settlements are analysed, and various procedural techniques proposed (including Voronoi diagrams, subdivision and L-systems) to replicate the identified features. A qualitative assessment of the procedural techniques is provided, and the most suitable combination of techniques identified for unstructured and structured settlements. In particular it is found that a combination of Voronoi diagrams and subdivision provides the closest match to unstructured informal settlements. A combination of L-systems, Voronoi diagrams and subdivision is found to produce the closest pattern to a structured informal settlement.
A large and persistent carbon sink in the world's forests.

The terrestrial carbon sink has been large in recent decades, but its size and location remain uncertain. Using forest inventory data and long-term ecosystem carbon studies, we estimate a total forest sink of 2.4 ± 0.4 petagrams of carbon per year (Pg C year(-1)) globally for 1990 to 2007. We also estimate a source of 1.3 ± 0.7 Pg C year(-1) from tropical land-use change, consisting of a gross tropical deforestation emission of 2.9 ± 0.5 Pg C year(-1) partially compensated by a carbon sink in tropical forest regrowth of 1.6 ± 0.5 Pg C year(-1). Together, the fluxes comprise a net global forest sink of 1.1 ± 0.8 Pg C year(-1), with tropical estimates having the largest uncertainties. Our total forest sink estimate is equivalent in magnitude to the terrestrial sink deduced from fossil fuel emissions and land-use change sources minus ocean and atmospheric sinks.
Understanding figurative and literal language : The graded salience hypothesis

In this study I lest the prevalent Claims among contemporary psycholinguists that understanding metaphor does not involve a special process, and that it is essentially identical to understanding literal language. Particularly, I examine the claims that figurative language does not involve processing the surface literal meaning (e.g., Gibbs 1984), and that its comprehension is not processing-intensive, because it does not involve a trigger (e.g., Keysar 1989). A critique, review and reinterpretation ofa number of contemporary researches on literal and figurative language reveal that figurative and literal language use are governed by a general principle of salience: Salient meanings (e.g., conventional frequent, familiär, enhanced by prior context) are processed first. Thus, for example, when the most salient meaning is intended (äs in, e.g., the figurative meaning of conventional Idioms), it is accessed directly, without having toprocess the less salient (literal) meaning first (Gibbs 1980). However, when a less rather than a more salient meaning is intended (e.g., the metaphoric meaning ofnovel metaphors, the literal meaning of conventional Idioms, or a novel Interpretation ofa highly conventional literal expression) comprehension seems to involve a sequential process, upon which the more salient meaning is processed initially, before the intended meaning is derived (Blasko and Connine 1993; Gerrig 1989; Gibbs 1980; Gregory and Mergler 1990). Parallel processing is induced when more than one meaning is salient. For instance, conventional metaphors whose metaphoric and literal meanings are equally salient, are processed initially both literally and metaphorically (Blasko and Connine 1993). The directl sequential process debate, then, can be reconciled: Different linguistic expressions ( salient-less salient) may tap different (direct/parallel/sequential) processes.
A Survey on Resource Scheduling in Cloud Computing: Issues and Challenges

Resource scheduling in cloud is a challenging job and the scheduling of appropriate resources to cloud workloads depends on the QoS requirements of cloud applications. In cloud environment, heterogeneity, uncertainty and dispersion of resources encounters problems of allocation of resources, which cannot be addressed with existing resource allocation policies. Researchers still face troubles to select the efficient and appropriate resource scheduling algorithm for a specific workload from the existing literature of resource scheduling algorithms. This research depicts a broad methodical literature analysis of resource management in the area of cloud in general and cloud resource scheduling in specific. In this survey, standard methodical literature analysis technique is used based on a complete collection of 110 research papers out of large collection of 1206 research papers published in 19 foremost workshops, symposiums and conferences and 11 prominent journals. The current status of resource scheduling in cloud computing is distributed into various categories. Methodical analysis of resource scheduling in cloud computing is presented, resource scheduling algorithms and management, its types and benefits with tools, resource scheduling aspects and resource distribution policies are described. The literature concerning to thirteen types of resource scheduling algorithms has also been stated. Further, eight types of resource distribution policies are described. Methodical analysis of this research work will help researchers to find the important characteristics of resource scheduling algorithms and also will help to select most suitable algorithm for scheduling a specific workload. Future research directions have also been suggested in this research work.
Personalized recommendation via cross-domain triadic factorization

Collaborative filtering (CF) is a major technique in recommender systems to help users find their potentially desired items. Since the data sparsity problem is quite commonly encountered in real-world scenarios, Cross-Domain Collaborative Filtering (CDCF) hence is becoming an emerging research topic in recent years. However, due to the lack of sufficient dense explicit feedbacks and even no feedback available in users' uninvolved domains, current CDCF approaches may not perform satisfactorily in user preference prediction. In this paper, we propose a generalized Cross Domain Triadic Factorization (CDTF) model over the triadic relation user-item-domain, which can better capture the interactions between domain-specific user factors and item factors. In particular, we devise two CDTF algorithms to leverage user explicit and implicit feedbacks respectively, along with a genetic algorithm based weight parameters tuning algorithm to trade off influence among domains optimally. Finally, we conduct experiments to evaluate our models and compare with other state-of-the-art models by using two real world datasets. The results show the superiority of our models against other comparative models.
Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks

Convolutional neural networks (CNNs) have recently been applied to the optical flow estimation problem. As training the CNNs requires sufficiently large amounts of labeled data, existing approaches resort to synthetic, unrealistic datasets. On the other hand, unsupervised methods are capable of leveraging real-world videos for training where the ground truth flow fields are not available. These methods, however, rely on the fundamental assumptions of brightness constancy and spatial smoothness priors that do not hold near motion boundaries. In this paper, we propose to exploit unlabeled videos for semi-supervised learning of optical flow with a Generative Adversarial Network. Our key insight is that the adversarial loss can capture the structural patterns of flow warp errors without making explicit assumptions. Extensive experiments on benchmark datasets demonstrate that the proposed semi-supervised algorithm performs favorably against purely supervised and baseline semi-supervised learning schemes.
Chasing Hypernyms in Vector Spaces with Entropy

In this paper, we introduce SLQS, a new entropy-based measure for the unsupervised identification of hypernymy and its directionality in Distributional Semantic Models (DSMs). SLQS is assessed through two tasks: (i.) identifying the hypernym in hyponym-hypernym pairs, and (ii.) discriminating hypernymy among various semantic relations. In both tasks, SLQS outperforms other state-of-the-art measures.
Evolutionary-based feature selection approaches with new criteria for data mining: A case study of credit approval data

In this paper, the feature selection problem was formulated as a multi-objective optimization problem, and new criteria were proposed to fulfill the goal. Foremost, data were pre-processed with missing value replacement scheme, re-sampling procedure, data type transformation procedure, and min-max normalization procedure. After that a wide variety of classifiers and feature selection methods were conducted and evaluated. Finally, the paper presented comprehensive experiments to show the relative performance of the classification tasks. The experimental results revealed the success of proposed methods in credit approval data. In addition, the numeric results also provide guides in selection of feature selection methods and classifiers in the knowledge discovery process.
Methods for the synthesis of qualitative research: a critical review

In recent years, a growing number of methods for synthesising qualitative research have emerged, particularly in relation to health-related research. There is a need for both researchers and commissioners to be able to distinguish between these methods and to select which method is the most appropriate to their situation.



A number of methodological and conceptual links between these methods were identified and explored, while contrasting epistemological positions explained differences in approaches to issues such as quality assessment and extent of iteration. Methods broadly fall into 'realist' or 'idealist' epistemologies, which partly accounts for these differences.



Methods for qualitative synthesis vary across a range of dimensions. Commissioners of qualitative syntheses might wish to consider the kind of product they want and select their method - or type of method - accordingly.
FlashMeta: a framework for inductive program synthesis

Inductive synthesis, or programming-by-examples (PBE) is gaining prominence with disruptive applications for automating repetitive tasks in end-user programming. However, designing, developing, and maintaining an effective industrial-quality inductive synthesizer is an intellectual and engineering challenge, requiring 1-2 man-years of effort. Our novel observation is that many PBE algorithms are a natural fall-out of one generic meta-algorithm and the domain-specific properties of the operators in the underlying domain-specific language (DSL). The meta-algorithm propagates example-based constraints on an expression to its subexpressions by leveraging associated witness functions, which essentially capture the inverse semantics of the underlying operator. This observation enables a novel program synthesis methodology called data-driven domain-specific deduction (D4), where domain-specific insight, provided by the DSL designer, is separated from the synthesis algorithm. Our FlashMeta framework implements this methodology, allowing synthesizer developers to generate an efficient synthesizer from the mere DSL definition (if properties of the DSL operators have been modeled). In our case studies, we found that 10+ existing industrial-quality mass-market applications based on PBE can be cast as instances of D4. Our evaluation includes reimplementation of some prior works, which in FlashMeta become more efficient, maintainable, and extensible. As a result, FlashMeta-based PBE tools are deployed in several industrial products, including Microsoft PowerShell 3.0 for Windows 10, Azure Operational Management Suite, and Microsoft Cortana digital assistant.
Acoustic analysis of the infant cry: classical and new methods

In the last few decades many papers have written about the analysis of the infant cry. The acoustic analysis has a shorter history than emotional, physiological, etc. investigations. This paper deals with classical and new methods of acoustic analysis of the infant cry. The final goal is to detect hearing disorders according to the crying at the earliest possible moment. Classical acoustic methods were reproduced and compared with solutions, which were not available before. This paper most of all deals with the characteristics of the fundamental frequency of the cry in the time and in the frequency domain.
A Latent Topic Model for Complete Entity Resolution

In bibliographies like DBLP and Citeseer, there are three kinds of entity-name problems that need to be solved. First, multiple entities share one name, which is called the name sharing problem. Second, one entity has different names, which is called the name variant problem. Third, multiple entities share multiple names, which is called the name mixing problem. We aim to solve these problems based on one model in this paper. We call this task complete entity resolution. Different from previous work, our work use global information based on data with two types of information, words and author names. We propose a generative latent topic model that involves both author names and words — the LDA-dual model, by extending the LDA (Latent Dirichlet Allocation) model. We also propose a method to obtain model parameters that is global information. Based on obtained model parameters, we propose two algorithms to solve the three problems mentioned above. Experimental results demonstrate the effectiveness and great potential of the proposed model and algorithms.
Minimal-redundancy-maximal-relevance feature selection using different relevance measures for omics data classification

Omics refers to a field of study in biology such as genomics, proteomics, and metabolomics. Investigating fundamental biological problems based on omics data would increase our understanding of bio-systems as a whole. However, omics data is characterized with high-dimensionality and unbalance between features and samples, which poses big challenges for classical statistical analysis and machine learning methods. This paper studies a minimal-redundancy-maximal-relevance (MRMR) feature selection for omics data classification using three different relevance evaluation measures including mutual information (MI), correlation coefficient (CC), and maximal information coefficient (MIC). A linear forward search method is used to search the optimal feature subset. The experimental results on five real-world omics datasets indicate that MRMR feature selection with CC is more robust to obtain better (or competitive) classification accuracy than the other two measures.
TiNA: a scheme for temporal coherency-aware in-network aggregation

This paper presents TiNA, a scheme for minimizing energy consumption in sensor networks by exploiting end-user tolerance to temporal coherency. TiNA utilizes temporal coherency tolerances to both reduce the amount of information transmitted by individual nodes (communication cost dominates power usage in sensor networks), and to improve quality of data when not all sensor readings can be propagated up the network within a given time constraint. TiNA was evaluated against a traditional in-network aggregation scheme with respect to power savings as well as the quality of data for aggregate queries. Preliminary results show that TiNA can reduce power consumption by up to 50% without any loss in the quality of data.
The acceptance and use of a virtual learning environment in China

The success of a virtual learning environment (VLE) depends to a considerable extent on student acceptance and use of such an e-learning system. After critically assessing models of technology adoption, including the Technology Acceptance Model (TAM), TAM2, and the Unified Theory of Acceptance and Usage of Technology (UTAUT), we build a conceptual model to explain the differences between individual students in the level of acceptance and use of a VLE. This model extends TAM2 and includes subjective norm, personal innovativeness in the domain of information technology, and computer anxiety. Data were collected from 45 Chinese participants in an Executive MBA program. After performing satisfactory reliability and validity checks, the structural model was tested with the use of PLS. Results indicate that perceived usefulness has a direct effect on VLE use. Perceived ease of use and subjective norm have only indirect effects via perceived usefulness. Both personal innovativeness and computer anxiety have direct effects on perceived ease of use only. Implications are that program managers in education should not only concern themselves with basic system design but also explicitly address individual differences between VLE users.
Adaptive Linear Step-up Procedures that control the False Discovery Rate

The linear step-up multiple testing procedure controls the False Discovery Rate (FDR) at the desired level q for independent and positively dependent test statistics. When all null hypotheses are true, and the test statistics are independent and continuous, the bound is sharp. When some of the null hypotheses are not true, the procedure is conservative by a factor which is the proportion m0/m of the true null hypotheses among the hypotheses. We provide a new two-stage procedure in which the linear step-up procedure is used in stage one to estimate m0, providing a new level q′ which is used in the linear step-up procedure in the second stage. We prove that a general form of the two-stage procedure controls the FDR at the desired level q. This framework enables us to study analytically the properties of other procedures that exist in the literature. A simulation study is presented that shows that two-stage adaptive procedures improve in power over the original procedure, mainly because they provide tighter control of the FDR. We further study the performance of the current suggestions, some variations of the procedures, and previous suggestions, in the case where the test statistics are positively dependent, a case for which the original procedure controls
MULTIDISCIPLINARY AEROSPACE DESIGN OPTIMIZATION: SURVEY OF RECENT DEVELOPMENTS

The increasing complexity of engineering systems has sparked increasing interest in multdisciplinary optimization (MDO). This paper presents a survey of recent publications in the field of aerospace where interest in MDO has been particularly intense. The two main challenges of MDO are computational expense and organizational complexity. Accordingly the survey is focused on various ways different researchers use to deal with these challenges. The survey is organized by a breakdown of MDO into its conceptual components. Accordingly, the survey includes sections on Mathematical Modeling, Design-oriented Analysis, Approximation Concepts, Optimization Procedures, System Sensitivity, and Human Interface. With the authors’ main expertise being in the structures area, the bulk of the references focus on the interaction of the structures discipline with other disciplines. In particular, two sections at the end focus on two such interactions that have recently been pursued with a particular vigor: Simultaneous Optimization of Structures and Aerodynamics, and Simultaneous Optimization of Structures Combined With Active Control.
Fast and Fully Automatic Ear Detection Using Cascaded AdaBoost

Ear detection from a profile face image is an important step in many applications including biometric recognition. But accurate and rapid detection of the ear for real-time applications is a challenging task, particularly in the presence of occlusions. In this work, a cascaded AdaBoost based ear detection approach is proposed. In an experiment with a test set of 203 profile face images, all the ears were accurately detected by the proposed detector with a very low (5 x 10-6) false positive rate. It is also very fast and relatively robust to the presence of occlusions and degradation of the ear images (e.g. motion blur). The detection process is fully automatic and does not require any manual intervention.
Less Is More: Towards Compact CNNs

We proposed an algorithm to significantly reduce of the number of neurons in a convolutional neural network by adding sparse constraints during the training step. The forward-backward splitting method is applied to solve the sparse constrained problem. We also analyze the benefits of using rectified linear units as non-linear activation function to remove a larger number of neurons. Experiments using four popular CNNs including AlexNet and VGG-B demonstrate the capacity of the proposed method to reduce the number of neurons, therefore, the number of parameters and memory footprint, with a negligible loss in performance.
Inside PageRank

Although the interest of a Web page is strictly related to its content and to the subjective readers' cultural background, a measure of the page authority can be provided that only depends on the topological structure of the Web. PageRank is a noticeable way to attach a score to Web pages on the basis of the Web connectivity. In this article, we look inside PageRank to disclose its fundamental properties concerning stability, complexity of computational scheme, and critical role of parameters involved in the computation. Moreover, we introduce a circuit analysis that allows us to understand the distribution of the page score, the way different Web communities interact each other, the role of dangling pages (pages with no outlinks), and the secrets for promotion of Web pages.
Gamification of Education: A Review of Literature

We synthesized the literature on gamification of education by conducting a review of the literature on gamification in the educational and learning context. Based on our review, we identified several game design elements that are used in education. These game design elements include points, levels/stages, badges, leaderboards, prizes, progress bars, storyline, and feedback. We provided examples from the literature to illustrate the application of gamification in the educational context.