nils-herrmann/clean_scirepeval huggingface dataset:
Research, design and fabrication of 2.45 GHz microstrip patch antenna arrays for close-range wireless power transmission systems

Antennas play a very important role in wireless power transmission (WPT) systems using microwave since they affect the system transmission efficiency. General requirements for an antenna used for WPT systems are high directivity and narrow beamwidth. This paper presents the design, simulation and fabrication of microstrip patch antenna arrays for close-range WPT systems. Various antenna prototypes such as single patch, 2×2, 2×4, 4×8 and 8×8 microstrip patch antenna arrays working at 2.45 GHz are proposed. The 2×4 microstrip patch antenna array is fabricated and measured using Anritsu 37364D Vector Network Analyzer and NSI2000 Nearfield System. Measurement results show that the fabricated antenna obtains a directivity of 14.7 dBi and a 3 dB beamwidth of 23.2 degree. The experiment with the WPT system is carried out. This paper also presents structure of a WPT system and the role each building block in the system.
CloudTax: A CloudSim-Extension for Simulating Tax Systems on Cloud Markets

Today service markets are becoming business reality as for example Amazon's EC2 spot market. However, current research focusses on simplified consumer-provider service markets only. Taxes are an important market element which has not been considered yet for service markets. This paper introduces and evaluates the effects of tax systems for IaaS markets which trade virtual machines. As a digital good with well defined characteristics like storage or processing power a virtual machine can be taxed by the tax authority using different tax systems. Currently the value added tax is widely used for taxing virtual machines only. The main contribution of the paper is the so called CloudTax component, a framework to simulate and evaluate different tax systems on service markets. It allows to introduce economical principles and phenomenons like the Laffer Curve or tax incidences. The CloudTax component is based on the CloudSim simulation framework using the Bazaar-Extension for comprehensive economic simulations. We show that tax mechanisms strongly influence the efficiency of negotiation processes in the Cloud market.
Pier – The Meta-Described Content Management System ESUG Innovation Technology Awards 2007

Pier is the second generation of an industrial strength content management and application framework. Pier is written with objects from top to bottom and it is easily customized to accommodate new requirements. Pier is based on Magritte, a powerful meta-description framework. Pier has proven to be very powerful in the combination with Seaside, to enable easy composition and configuration of interactive web sites through a convenient web interface without having to write code.
FILIPINO PHILOSOPHY : A WESTERN TRADITION IN AN EASTERN SETTING

[This paper is the third revised version (2013). It was originally presented in a philosophical conference in Athens, Greece on 6 June 2006, Athens Institute of Education and Research. It was first published as Chapter 28 in The philosophical landscape. Third edition. Edited by Rolando M. Gripaldo.  Philippine National Philosophical Research Society, 2007. Other editions appeared in Philosophia: International Journal of Philosophy 36/8 (1): January 2007 and in The making of a Filipino philosopher and other essays. A collection of Gripaldo’s essays.] Chapter 2. Mandaluyong City: National Book Store, 2009.
"People Are Either Too Fake or Too Real": Opportunities and Challenges in Tie-Based Anonymity

In recent years, several mobile applications allowed individuals to anonymously share information with friends and contacts, without any persistent identity marker. The functions of these "tie-based" anonymity services may be notably different than other social media services. We use semi-structured interviews to qualitatively examine motivations, practices and perceptions in two tie-based anonymity apps: Secret (now defunct, in the US) and Mimi (in China). Among the findings, we show that: (1) while users are more comfortable in self-disclosure, they still have specific practices and strategies to avoid or allow identification; (2) attempts for deidentification of others are prevalent and often elaborate; and (3) participants come to expect both negativity and support in response to posts. Our findings highlight unique opportunities and potential benefits for tie-based anonymity apps, including serving disclosure needs and social probing. Still, challenges for making such applications successful, for example the prevalence of negativity and bullying, are substantial.
A review of curanderismo and healing practices among Mexicans and Mexican Americans.

Occupational therapists working with Mexican and Mexican American populations may encounter traditional healing practices associated with curanderismo within a variety of practice settings. Curanderismo is a term referring to the practice of traditional healing in Latin American (Hispanic) cultures. This article reviews from the literature the different types of traditional healers (curanderos/as), the remedies recommended by traditional healers and common traditional illnesses treated. Traditional healing practices among Mexican and Mexican Americans may be as high as 50-75% in some parts of the United States. Further research is needed to investigate the effectiveness of curanderismo and its impact on quality of life, activities of daily living and overall social participation.
Efficient sample reuse in policy search by multiple importance sampling

Policy search such as reinforcement learning and evolutionary computation is a framework for finding an optimal policy of control problems, but it usually requires a huge number of samples. Importance sampling is a common tool to use samples drawn from a proposal distribution different from the targeted one, and it is widely used by the policy search methods to update the policy from a set of datasets that are collected by previous sampling distributions. However, the proposal distribution is created by a mixture of previous distributions with fixed mixing weights in most of previous studies, and it is often numerically unstable. To overcome this problem, we propose the method of adaptive multiple importance sampling that optimizes the mixing coefficients to minimize the variance of the importance sampling estimator while utilizing as many samples as possible. We apply the proposed method to the five policy search methods such as PGPE, PoWER, CMA-ES, REPS, and NES, and their algorithms are evaluated by some benchmark control tasks. Experimental results show that all the five methods improve sample efficiency. In addition, we show that optimizing the mixing weights achieves stable learning.
A survey of intrusion detection systems for cloud computing environment

Cloud computing is a newly emerged technology, and the rapidly growing field of IT. It is used extensively to deliver Computing, data Storage services and other resources remotely over internet on a pay per usage model. Nowadays, it is the preferred choice of every IT organization because it extends its ability to meet the computing demands of its everyday operations, while providing scalability, mobility and flexibility with a low cost. However, the security and privacy is a major hurdle in its success and its wide adoption by organizations, and the reason that Chief Information Officers (CIOs) hesitate to move the data and applications from premises of organizations to the cloud. In fact, due to the distributed and open nature of the cloud, resources, applications, and data are vulnerable to intruders. Intrusion Detection System (IDS) has become the most commonly used component of computer system security and compliance practices that defends network accessible Cloud resources and services from various kinds of threats and attacks. This paper presents an overview of different intrusions in cloud, various detection techniques used by IDS and the types of Cloud Computing based IDS. Then, we analyze some pertinent existing cloud based intrusion detection systems with respect to their various types, positioning, detection time and data source. The analysis also gives strengths of each system, and limitations, in order to evaluate whether they carry out the security requirements of cloud computing environment or not. We highlight the deployment of IDS that uses multiple detection approaches to deal with security challenges in cloud.
Breast cancer diagnosis using adaptive voting ensemble machine learning algorithm

According to Breast Cancer Institute (BCI), Breast Cancer is one of the most dangerous type of diseases that is very effective for women in the world. As per clinical expert detecting this cancer in its first stage helps in saving lives. As per cancer.net offers individualized guides for more than 120 types of cancer and related hereditary syndromes. For detecting breast cancer mostly machine learning techniques are used. In this paper we proposed adaptive ensemble voting method for diagnosed breast cancer using Wisconsin Breast Cancer database. The aim of this work is to compare and explain how ANN and logistic algorithm provide better solution when its work with ensemble machine learning algorithms for diagnosing breast cancer even the variables are reduced. In this paper we used the Wisconsin Diagnosis Breast Cancer dataset. When compared to related work from the literature. It is shown that the ANN approach with logistic algorithm is achieved 98.50% accuracy from another machine learning algorithm.
Generalized Functional Linear Models with Semiparametric Single-Index Interactions.

We introduce a new class of functional generalized linear models, where the response is a scalar and some of the covariates are functional. We assume that the response depends on multiple covariates, a finite number of latent features in the functional predictor, and interaction between the two. To achieve parsimony, the interaction between the multiple covariates and the functional predictor is modeled semiparametrically with a single-index structure. We propose a two step estimation procedure based on local estimating equations, and investigate two situations: (a) when the basis functions are pre-determined, e.g., Fourier or wavelet basis functions and the functional features of interest are known; and (b) when the basis functions are data driven, such as with functional principal components. Asymptotic properties are developed. Notably, we show that when the functional features are data driven, the parameter estimates have an increased asymptotic variance, due to the estimation error of the basis functions. Our methods are illustrated with a simulation study and applied to an empirical data set, where a previously unknown interaction is detected. Technical proofs of our theoretical results are provided in the online supplemental materials.
RDF Query Answering Using Apache Spark: Review and Assessment

The explosion of the web and the abundance of linked data demand for effective and efficient methods for storage, management and querying. More specifically, the ever-increasing size and number of RDF data collections raises the need for efficient query answering, and dictates the usage of distributed data management systems for effectively partitioning and querying them. To this direction, Apache Spark is one of the most active big-data approaches, with more and more systems adopting it, for efficient, distributed data management. The purpose of this paper is to provide an overview of the existing works dealing with efficient query answering, in the area of RDF data, using Apache Spark. We discuss on the characteristics and the key dimension of such systems, we describe novel ideas in the area, and the corresponding drawbacks, and provide directions for future work.
AN IMPLEMENTATION OF LOAD BALANCING ALGORITHM IN CLOUD ENVIRONMENT

7124 | P a g e C o u n c i l f o r I n n o v a t i v e R e s e a r c h J u l y , 2 0 1 6 w w w . c i r w o r l d . c o m AN IMPLEMENTATION OF LOAD BALANCING ALGORITHM IN CLOUD ENVIRONMENT Sheenam Kamboj , Mr. Navtej Singh Ghumman (2) (1) Research Scholar, Department of Computer Science & Engineering, SBSSTC, Ferozepur, Punjab. sheenam31.sk@gmail.com (2) Assistant Professor, Department of Computer Science & Engineering, SBSSTC, Ferozepur, Punjab.
Growth performance, feed utilization, digestive enzyme activity, innate immunity and protection against Vibrio harveyi of freshwater prawn, Macrobrachium rosenbergii fed diets supplemented with Bacillus coagulans

The present study was conducted to investigate the effects of dietary supplementation of Bacillus coagulans on growth, feed utilization, digestive enzyme activity, innate immune response and disease resistance of freshwater prawn Macrobrachium rosenbergii. Three treatment groups (designated as T1, T2 and T3) and a control group (C), each in triplicates, were established. The prawn in the control were fed a basal diet and those in T1, T2 and T3 were fed basal diet containing B. coagulans at 105, 107 and 109 cfu g−1, respectively. After 60 days, growth performance and feed utilization were found significantly higher (P < 0.05) in prawn fed T3 diet. The specific activities of protease, amylase and lipase digestive enzymes were significantly higher (P < 0.05) for T3. Innate immunity in terms of lysozyme and respiratory burst activities were significantly elevated (P < 0.05) in all the probiotic treatment groups as compared to control. Challenge study with Vibrio harveyi revealed significant increase (P < 0.05) in disease resistance of freshwater prawn in T2 and T3 groups. The results collectively suggested that supplementation of B. coagulans as probiotic in the diet at approximately 109 cfu g−1 can improve the growth performance, feed utilization, digestive enzyme activity, innate immune response and disease resistance of freshwater prawn.
Fusion of neural networks with fuzzy logic and genetic algorithm

Combining multiple estimators has appeared as one of hot research topics in several areas including artificial neural networks. This paper presents three methods to work out the problem based on so-called softcomputing techniques, toward a unified framework of hybrid softcomputing techniques. The first method based on fuzzy logic nonlinearly combines objective evidences, in the form of network outputs, with subjective evaluation of the reliability of the individual neural networks. The second method based on genetic algorithm gives us an effective vehicle to determine the optimal weight parameters that are multiplied by the network outputs. Finally, we have proposed a hybrid synergistic method of fuzzy logic and genetic algorithm to optimally combine neural networks. The experimental results with the recognition problem of totally unconstrained handwritten digits show that the performance could be improved significantly with the proposed softcomputing techniques.
Interactive software maps for web-based source code analysis

Software maps -- linking rectangular 3D-Treemaps, software system structure, and performance indicators -- are commonly used to support informed decision making in software-engineering processes. A key aspect for this decision making is that software maps provide the structural context required for correct interpretation of these performance indicators. In parallel, source code repositories and collaboration platforms are an integral part of today's software-engineering tool set, but cannot properly incorporate software maps since implementations are only available as stand-alone applications. Hence, software maps are 'disconnected' from the main body of this tool set, rendering their use and provisioning overly complicated, which is one of the main reasons against regular use. We thus present a web-based rendering system for software maps that achieves both fast client-side page load time and interactive frame rates even with large software maps. We significantly reduce page load time by efficiently encoding hierarchy and geometry data for the net transport. Apart from that, appropriate interaction, layouting, and labeling techniques as well as common image enhancements aid evaluation of project-related quality aspects. Metrics provisioning can further be implemented by predefined attribute mappings to simplify communication of project specific quality aspects. The system is integrated into dashboards to demonstrate how our web-based approach makes software maps more accessible to many different stakeholders in software-engineering projects.
A Simulation Environment for Middle-Size Robots with Multi-level Abstraction

Larger fields in the Middle-size league as well as the effort to build mixed teams from different universities require a simulation environment which is capable to physically correctly simulate the robots and the environment. A standardized simulation environment has not yet been proposed for this league. In this paper we present our simulation environment, which is based on the Gazebo system. We show how typical Middle-size robots with features like omni-drives and omni-directional cameras can be modeled with relative ease. In particular, the control software for the real robots can be used with few changes, thus facilitating the transfer of results obtained in simulation back to the robots. We address some technical issues such as adapting time-triggered events in the robot control software to the simulation, and we introduce the concept of multi-level abstractions. The latter allows switching between faithful but computionally expensive sensor models and abstract but cheap approximations. These abstractions are needed especially when simulating whole teams of robots.
Implementation of 3-axis linear interpolation in a FPGA-based 4-axis motion controller

To carry out the motion control of CNC machine and robot, this paper introduces an approach to implement 4-axis motion controller based on field programmable gate array (FPGA). Starting with introduction to existing excellent 4-axis motion controller MCX314, the fundamental structure of controller is discussed. Since the straight-line motion is a fundamental motion of CNC machine and robot, this paper introduces a linear interpolation method to do approximate straight-line motion within any 3-axis space. As Interpolation calculation of hardware interpolation is implemented by hardware logic circuit such as ASIC or FPGA in the controller, therefore this method can avoid a large amount of complex mathematical calculation, which hints that this controller has high real-time performance. The simulation of straight-line motion within 3D space verifies the effectiveness of the proposed approach.
Light it up: using paper circuitry to enhance low-fidelity paper prototypes for children

Paper prototyping is an important tool for designing and testing early technologies during development. However, children have different relationships with technology and thus one cannot expect children to assess paper prototypes with the same mental model as adults. In this paper, we examine the effect of incorporating paper circuitry into low-fidelity paper prototypes, in order to add a level of interactivity that is not present in traditional paper prototypes. We conducted a study with 20 children ages 3 to 10 years old where participants used a cardboard prototype of a voice-controlled rocket on a pretend play mission to Mars. Children chose between buttons that lit up when pressed using paper circuitry, and buttons that did not light up, and explained their selections to the researchers. Our results show that children indeed preferred buttons augmented with paper circuitry, demonstrating more attention for and increased believability in the function of these buttons as well as the overall system. These findings show how designers can use paper circuitry to more effectively engage children while play-testing their paper prototypes.
Relevance Factor VAE: Learning and Identifying Disentangled Factors

We propose a novel VAE-based deep autoencoder model that can learn disentangled latent representations in a fully unsupervised manner, endowed with the ability to identify all meaningful sources of variation and their cardinality. Our model, dubbed Relevance-Factor-VAE, leverages the total correlation (TC) in the latent space to achieve the disentanglement goal, but also addresses the key issue of existing approaches which cannot distinguish between meaningful and nuisance factors of latent variation, often the source of considerable degradation in disentanglement performance. We tackle this issue by introducing the so-called relevance indicator variables that can be automatically learned from data, together with the VAE parameters. Our model effectively focuses the TC loss onto the relevant factors only by tolerating large prior KL divergences, a desideratum justified by our semi-parametric theoretical analysis. Using a suite of disentanglement metrics, including a newly proposed one, as well as qualitative evidence, we demonstrate that our model outperforms existing methods across several challenging benchmark datasets.
Transparent user models for personalization

Personalization is a ubiquitous phenomenon in our daily online experience. While such technology is critical for helping us combat the overload of information we face, in many cases, we may not even realize that our results are being tailored to our personal tastes and preferences. Worse yet, when such a system makes a mistake, we have little recourse to correct it.
 In this work, we propose a framework for addressing this problem by developing a new user-interpretable feature set upon which to base personalized recommendations. These features, which we call badges, represent fundamental traits of users (e.g., "vegetarian" or "Apple fanboy") inferred by modeling the interplay between a user's behavior and self-reported identity. Specifically, we consider the microblogging site Twitter, where users provide short descriptions of themselves in their profiles, as well as perform actions such as tweeting and retweeting. Our approach is based on the insight that we can define badges using high precision, low recall rules (e.g., "Twitter profile contains the phrase 'Apple fanboy'"), and with enough data, generalize to other users by observing shared behavior. We develop a fully Bayesian, generative model that describes this interaction, while allowing us to avoid the pitfalls associated with having positive-only data.
 Experiments on real Twitter data demonstrate the effectiveness of our model at capturing rich and interpretable user traits that can be used to provide transparency for personalization.
Mouse color and wavelength-specific luminance contrast sensitivity are non-uniform across visual space

Mammalian visual behaviors, as well as responses in the neural systems underlying these behaviors, are driven by luminance and color contrast. With constantly improving tools for measuring activity in cell-type-specific populations in the mouse during visual behavior, it is important to define the extent of luminance and color information that is behaviorally accessible to the mouse. A non-uniform distribution of cone opsins in the mouse retina potentially complicates both luminance and color sensitivity; opposing gradients of short (UV-shifted) and middle (blue/green) cone opsins suggest that color discrimination and wavelength-specific luminance contrast sensitivity may differ with retinotopic location. Here we ask how well mice can discriminate color and wavelength-specific luminance changes across visuotopic space. We found that mice were able to discriminate color and were able to do so more broadly across visuotopic space than expected from the cone-opsin distribution. We also found wavelength-band-specific differences in luminance sensitivity.