nils-herrmann/clean_scirepeval huggingface dataset:
Design of quadrifilar spiral antenna with integrated module for UHF RFID reader

In this paper, a quadrifilar spiral antenna (QSA) with an integrated module for UHF radio frequency identification (RFID) reader is presented. The proposed QSA consists of four spiral antennas with short stubs and a microstrip feed network. Also, the shielded module is integrated on the center of the ground inside the proposed QSA. In order to match the proposed QSA with the integrated module, we adopt a short stub connected from each spiral antenna to ground. Experimental result shows that the QSA of size 80 × 80 × 11.2 mm3 with the integrated module (40 × 40 × 3 mm3) has a peak gain of 3.5 dBic, an axial ratio under 2.5 dB and a 3-dB beamwidth of about 130o.
Explaining Differences between Groups: Oaxaca Decomposition

After inequalities in the health sector are measured, a natural next step is to seek to explain them. Why do inequalities in health exist between the poor and better-off in many countries despite health systems explicitly aimed at eliminating inequalities in access to health care? Why is inequality in the incidence of health sector subsidies greater in one country than in another? Why has the distribution of health or health care changed over time? In this chapter and the next, we consider methods of decomposing inequality in health or health care into contributing factors. The core idea is to explain the distribution of the outcome variable in question by a set of factors that vary systematically with socioeconomic status. For example, variations in health may be explained by variations in education, income, insurance coverage, distance to health facilities, and quality of care at local facilities. Even if policy makers have managed to eliminate inequalities in some of these dimensions, inequalities between the poor and better-off may remain in others. The decomposition methods reveal how far inequalities in health can be explained by inequalities in, say, insurance coverage rather than inequalities in, say, distance to health facilities. The decompositions in this chapter and the next are based on regression analysis of the relationships between the health variable of interest and its correlates. Such analyses are usually purely descriptive, revealing the associations that characterize the health inequality, but if data are suffi cient to allow the estimation of causal effects, then it is possible to identify the factors that generate inequality in the variable of interest. In cases in which causal effects have not been obtained, the decomposition provides an explanation in the statistical sense, and the results will not necessarily be a good guide to policy making. For example, the results will not help us predict how inequalities in Y would change if policy makers were to reduce inequalities in X, or reduce the effect of X and Y (e.g., by expanding facilities serving remote populations if X were distance to provider). By contrast, if causal effects have been obtained, the decomposition results ought to shed light on such issues. The decomposition method outlined in this chapter, known as the Oaxaca decomposition (Oaxaca 1973), explains the gap in the means of an outcome variable between two groups (e.g., between the poor and the nonpoor). The gap is decomposed into that part …
Using Action Research Approach to Develop Teachers' Digital Skills: the Initial Framework

This paper summarizes theoretical framework and preliminary data for a planned action research project in a secondary education institution with the intention to improve teachers` digital skills and capacity for educational ICT use through establishment of a professional learning community and development of cooperation between the school and a university. This study aims to fill the gap of knowledge about how engaging in professional learning communities (PLC) fosters teachers` skills and confidence with ICT. Based on the theoretical assumptions and review of previous research, initial ideas are drafted for an action research project.
Modeling Input Validation in UML

Security is an integral part of most software systems but it is not considered as an explicit part in the development process yet. Input validation is the most critical part of software security that is not covered in the design phase of software development life-cycle resulting in many security vulnerabilities. Our objective is to extend UML to new integrated framework for model driven security engineering leading to ideal way to design more secure software. Input validation in UML has not been addressed previously, hence we incorporate input validation into UML diagrams such as use case, class, sequence and activity. This approach has some advantages such as preventing from common input tampering attacks, having both security and convenience in software at high level of abstraction and ability of solving the problem of weak security background for developers.
Synergy, Redundancy and Common Information

We consider the problem of decomposing the total mutual information conveyed by a pair of predictor random variables about a target random variable into redundant, uniqueand synergistic contributions. We focus on the relationship be tween “redundant information” and the more familiar information theoretic notions of “common information.” Our main contri bution is an impossibility result. We show that for independent predictor random variables, any common information based measure of redundancy cannot induce a nonnegative decompositi on of the total mutual information. Interestingly, this entai ls that any reasonable measure of redundant information cannot be deri ved by optimization over a single random variable. common and private information, synergy, redundancy, information lattice, sufficient statistic, partial information decomposition
SHA-less pipelined ADC converting 10th Nyquist band with in-situ clock-skew calibration

Conversion from dc to the 10th Nyquist band is enabled in a SHA-less, 10-b, 100-MS/s pipelined ADC by digitally calibrating the clock skew in the 3.5-b front-end stage. Architectural redundancy of pipelined ADC is exploited to extract skew information from the first-stage residue output with two out-of-range comparators and some simple digital logic; a gradient-descent algorithm is used to adaptively adjust the timing of the front-end sub-ADC to synchronize with that of the S/H. The 90-nm prototype consumes 12.2 mW and digitizes inputs up to 480 MHz (limited by testing equipment) without skew errors in experiments, whereas the same ADC fails at 130 MHz when the calibration is disabled. The measured SFDR is 71 dB at 20 MHz and 55 dB at 480 MHz.
Action Language BC +

Action languages are formal models of parts of natural language that are designed to describe effects of actions. Many of these languages can be viewed as high level notations of answer set programs structured to represent transition systems. However, the form of answer set programs considered in the earlier work is quite limited in comparison with the modern Answer Set Programming (ASP) language, which allows several useful constructs for knowledge representation, such as choice rules, aggregates, and abstract constraint atoms. We propose a new action language called BC+, which closes the gap between action languages and the modern ASP language. The main idea is to define the semantics of BC+ in terms of general stable model semantics for propositional formulas, under which many modern ASP language constructs can be identified with shorthands for propositional formulas. Language BC+ turns out to be sufficiently expressive to encompass the best features of other action languages, such as languages B, C, C+, and BC. Computational methods available in ASP solvers are readily applicable to compute BC+, which led to an implementation of the language by extending system CPLUS2ASP.
Narcissism in the Workplace and its Effects on an Organization

This report explores the relationship between narcissism and unethical conduct in an organization by answering two questions: (1) In what ways does narcissism affect an organization?, and (2) What is the relationship between narcissism and the financial industry? Research suggests the overall conclusion that narcissistic individuals directly influence the identity of an organization and how it behaves. Ways to address these issues are shown using Enron as a case study example.
Induction of anesthesia in a combative child; management and issues.

A developmentally delayed, 13-year old autistic boy required management of multifocal cerebral and pulmonary tumors, involving several anesthetics over a 4-month period. At each anesthetic he refused premedication, displayed increasing anxiety and became more combative. With parental guidance and involvement, a variety of anesthetists tried a range of techniques to achieve induction, each ultimately resorting to the use of physical restraint. Principles essential to the care of such a child include early recognition, parental support, multi-disciplinary planning of procedures requiring general anesthesia, continuity of anesthesia care, and clear guidelines about the perioperative management of uncooperative children, including the ethical use of restraint.
Text Categorization with Support Vector Machines: Learning with Many Relevant Features

This paper explores the use of Support Vector Machines (SVMs) for learning text classifiers from examples. It analyzes the particular properties of learning with text data and identifies why SVMs arc appropriate for this task. Empirical results support the theoretical findings. SVMs achieve substantial improvements over the currently best performing methods and behave robustly over a variety of different learning tasks. Furthermore, they are fully automatic, eliminating the need for manual parameter tuning. 1 I n t r o d u c t i o n With the rapid growth of online information, text categorization has become one of the key techniques for handling and organizing text data. Text categorization techniques are used to classify news stories, to find interesting information on the WWW, and to guide a user's search through hypertext. Since building text classifiers by hand is difficult and time-consuming, it is advantageous to learn classifiers from examples. In this paper I will explore and identify the benefits of Support Vector Machines (SVMs) for text categorization. SVMs are a new learning method introduced by V. Vapnik et al.  . They are well-founded in terms of computational learning theory and very open to theoretical understanding and analysis. After reviewing the standard feature vector representation of text, I will identify the particular properties of text in this representation in section 4. I will argue that SVMs are very well suited for learning in this setting. The empirical results in section 5 will support this claim. Compared to state-of-the-art methods, SVMs show substantial performance gains. Moreover, in contrast to conventional text classification methods SVMs will prove to be very robust, eliminating the need for expensive parameter tuning. 2 T e x t C a t e g o r i z a t i o n The goal of text categorization is the classification of documents into a fixed number of predefined categories. Each document can be in multiple, exactly one, or no category at all. Using machine learning, the objective is to learn classifiers
Optimizing VM placement for HPC in the cloud

Computing as a service model in cloud has encouraged High Performance Computing to reach out to wider scientific and industrial community. Many small and medium scale HPC users are exploring Infrastructure cloud as a possible platform to run their applications. However, there are gaps between the characteristic traits of an HPC application and existing cloud scheduling algorithms. In this paper, we propose an HPC-aware scheduler and implement it atop Open Stack scheduler. In particular, we introduce topology awareness and consideration for homogeneity while allocating VMs. We demonstrate the benefits of these techniques by evaluating them on a cloud setup on Open Cirrus test-bed.
Qualitative comparison of open-source SDN controllers

Telecommunication companies with expensive networks may become the biggest beneficiaries of SDN; however, in contrast to traditional routers, the development of SDN controllers is driven by open-source projects with involvement of the industry. Two prevalent projects in SDN development are the OpenDaylight and the ONOS controllers. These SDN controllers are advanced in their development - having gone through a number of releases - and have been described as being useful for a large number of use-cases. In this work, we compare and evaluate these controllers, in particular their northbound interfaces, by configuring them for a representative use-case, port-mirroring.
Re-ranking search results using semantic similarity

In this paper, we propose a re-ranking method which employs semantic similarity to improve the quality of search results. We fetch the top N results returned by search engine, and use semantic similarities between the candidate and the query to re-rank the results. We first convert the ranking position to an importance score for each candidate. Then we combine the semantic similarity score with this initial importance score and finally we get the new ranks. In the experiment, we use NDCG to evaluate the re-ranking results and the experimental results validate that our proposed method can indeed improve the search performance and meet users' need to a certain extent.
Prefixes and the Delimitation of Events *

In Slavic languages, verbal prefixes can be applied to perfective verbs deriving new perfective verbs, and multiple prefixes can occur in a single verb. This well-known type of data has not yet been adequately analyzed within current approaches to the semantics of Slavic verbal prefixes and aspect. The notion “aspect” covers “grammatical aspect”, or “viewpoint aspect” (see Smith 1991/1997), best characterized by the formal perfective vs. imperfective distinction, which is often expressed by inflectional morphology (as in Romance languages), and corresponds to propositional operators at the semantic level of representation. It also covers “lexical aspect”, “situation aspect” (see Smith ibid.), “eventuality types” (Bach 1981, 1986), or “Aktionsart” (as in Hinrichs 1985; Van Valin 1990; Dowty 1999; Paslawska and von Stechow 2002, for example), which regards the telic vs. atelic distinction and its Vendlerian subcategories (activities, accomplishments, achievements and states). It is lexicalized by verbs, encoded by derivational morphology, or by a variety of elements at the level of syntax, among which the direct object argument has a prominent role, however, the subject (external) argument is arguably a contributing factor, as well (see Dowty 1991, for example). These two “aspect” categories are orthogonal to each other and interact in systematic ways (see also Filip 1992, 1997, 1993/99; de Swart 1998; Paslawska and von Stechow 2002; Rothstein 2003, for example). Multiple prefixation and application of verbal prefixes to perfective bases is excluded by the common view of Slavic prefixes, according to which all perfective verbs are telic and prefixes constitute a uniform class of “perfective” markers that that are applied to imperfective verbs that are atelic and derive perfective verbs that are telic. Moreover, this view of perfective verbs and prefixes predicts rampant violations of the intuitive “one delimitation per event” constraint, whenever a prefix is applied to a perfective verb. This intuitive constraint is motivated by the observation that an event expressed within a single predication can be delimited only once: cp. *run a mile for ten minutes, *wash the clothes clean white.
Random Faces Guided Sparse Many-to-One Encoder for Pose-Invariant Face Recognition

One of the most challenging task in face recognition is to identify people with varied poses. Namely, the test faces have significantly different poses compared with the registered faces. In this paper, we propose a high-level feature learning scheme to extract pose-invariant identity feature for face recognition. First, we build a single-hidden-layer neural network with sparse constraint, to extract pose-invariant feature in a supervised fashion. Second, we further enhance the discriminative capability of the proposed feature by using multiple random faces as the target values for multiple encoders. By enforcing the target values to be unique for input faces over different poses, the learned high-level feature that is represented by the neurons in the hidden layer is pose free and only relevant to the identity information. Finally, we conduct face identification on CMU Multi-PIE, and verification on Labeled Faces in the Wild (LFW) databases, where identification rank-1 accuracy and face verification accuracy with ROC curve are reported. These experiments demonstrate that our model is superior to other state-of-the-art approaches on handling pose variations.
Banana cultivars, cultivation practices, and physicochemical properties.

The physicochemical (pH, texture, Vitamin C, ash, fat, minerals) and sensory properties of banana were correlated with the genotype and growing conditions. Minerals in particular were shown to discriminate banana cultivars of different geographical origin quite accurately. Another issue relates to the beneficial properties of bananas both in terms of the high dietary fiber and antioxidant compounds, the latter being abundant in the peel. Therefore, banana can be further exploited for extracting several important components such as starch, and antioxidant compounds which can find industrial and pharmaceutical applications. Finally, the various storage methodologies were presented with an emphasis on Modified Atmosphere Packaging which appears to be one of the most promising of technologies.
A Fusion of Deep Convolutional Generative Adversarial Networks and Sequence to Sequence Autoencoders for Acoustic Scene Classification

Unsupervised representation learning shows high promise for generating robust features for acoustic scene analysis. In this regard, we propose and investigate a novel combination of features learnt using both a deep convolutional generative adversarial network (DCGAN) and a recurrent sequence to sequence autoencoder (S2SAE). Each of the representation learning algorithms is trained individually on spectral features extracted from audio instances. The learnt representations are: (i) the activations of the discriminator in case of the DCGAN, and (ii) the activations of a fully connected layer between the decoder and encoder units in case of the S2SAE. We then train two multilayer perceptron neural networks on the DCGAN and S2SAE feature vectors to predict the class labels. The individual predicted labels are combined in a weighted decision-level fusion to achieve the final prediction. The system is evaluated on the development partition of the acoustic scene classification data set of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE 2017). In comparison to the baseline, the accuracy is increased from 74.8 % to 86.4 % using only the DCGAN, to 88.5 % on the development set using only the S2SAE, and to 91.1 % after fusion of the individual predictions.
Health benefits of anthocyanins and molecular mechanisms: Update from recent decade.

Anthocyanins are one of the most widespread families of natural pigments in the plant kingdom. Their health beneficial effects have been documented in many in vivo and in vitro studies. This review summarizes the most recent literature regarding the health benefits of anthocyanins and their molecular mechanisms. It appears that several signaling pathways, including mitogen-activated protein kinase, nuclear factor κB, AMP-activated protein kinase, and Wnt/β-catenin, as well as some crucial cellular processes, such as cell cycle, apoptosis, autophagy, and biochemical metabolism, are involved in these beneficial effects and may provide potential therapeutic targets and strategies for the improvement of a wide range of diseases in future. In addition, specific anthocyanin metabolites contributing to the observed in vivo biological activities, structure-activity relationships as well as additive and synergistic efficacy of anthocyanins are also discussed.
Performance evaluation of NewSQL databases

For over forty years, relational databases have been the leading model for data storage, retrieval and management. However, due to increasing needs for scalability and performance, alternative systems have emerged, namely NewSQL technology. NewSQL is a class of modern relational database management systems (RDBMS) that provide the same scalable performance of NoSQL systems for online transaction processing (OLTP) read-write workloads while still maintaining the ACID guarantees of a traditional database system. The rising interest in NewSQL technology, over the last few years resulted in an increasing number of evaluations and comparisons among competing NewSQL technologies. Some of the NewSQL databases currently used in various popular web applications are VoltDB, Google Spanner, MemSQL, SAP HANA, NuoDB, and TokuDB. This work is trying to comment on the various NewSQL database systems which describes benefits, characteristics, and classification of NewSQL databases for online transaction processing (OLTP) for Big data management. It also provides the list of popular NewSQL databases in categorized tables. This dissertation work mainly covers evaluation comparison between four NewSQL databases: NuoDB, VoltDB, MemSQL, and Cockroach DB on the basis of various parameters like read latency, write latency, update latency, and execution time. The experiments do not cover only performance (latency and execution time) but also focus on ease of use and flexibility of the used NewSQL databases.
Adoptive immunotherapy for indolent non-Hodgkin lymphoma and mantle cell lymphoma using genetically modified autologous CD20-specific T cells.

Adoptive immunotherapy with T cells expressing a tumor-specific chimeric T-cell receptor is a promising approach to cancer therapy that has not previously been explored for the treatment of lymphoma in human subjects. We report the results of a proof-of-concept clinical trial in which patients with relapsed or refractory indolent B-cell lymphoma or mantle cell lymphoma were treated with autologous T cells genetically modified by electroporation with a vector plasmid encoding a CD20-specific chimeric T-cell receptor and neomycin resistance gene. Transfected cells were immunophenotypically similar to CD8(+) effector cells and showed CD20-specific cytotoxicity in vitro. Seven patients received a total of 20 T-cell infusions, with minimal toxicities. Modified T cells persisted in vivo 1 to 3 weeks in the first 3 patients, who received T cells produced by limiting dilution methods, but persisted 5 to 9 weeks in the next 4 patients who received T cells produced in bulk cultures followed by 14 days of low-dose subcutaneous interleukin-2 (IL-2) injections. Of the 7 treated patients, 2 maintained a previous complete response, 1 achieved a partial response, and 4 had stable disease. These results show the safety, feasibility, and potential antitumor activity of adoptive T-cell therapy using this approach.