nils-herrmann/clean_scirepeval huggingface dataset:
Error vector magnitude to SNR conversion for nondata-aided receivers

Error vector magnitude (EVM) is one of the widely accepted figure of merits used to evaluate the quality of communication systems. In the literature, EVM has been related to signal-to-noise ratio (SNR) for data-aided receivers, where preamble sequences or pilots are used to measure the EVM, or under the assumption of high SNR values. In this paper, this relation is examined for nondata-aided receivers and is shown to perform poorly, especially for low SNR values or high modulation orders. The EVM for nondata-aided receivers is then evaluated and its value is related to the SNR for quadrature amplitude modulation (QAM) and pulse amplitude modulation (PAM) signals over additive white Gaussian noise (AWGN) channels and Rayleigh fading channels, and for systems with IQ imbalances. The results show that derived equations can be used to reliably estimate SNR values using EVM measurements that are made based on detected data symbols. Thus, presented work can be quite useful for measurement devices such as vector signal analyzers (VSA), where EVM measurements are readily available.
Adolescent peer relations, friendships, and romantic relationships: do they predict social anxiety and depression?

This study examined multiple levels of adolescents' interpersonal functioning, including general peer relations (peer crowd affiliations, peer victimization), and qualities of best friendships and romantic relationships as predictors of symptoms of depression and social anxiety. An ethnically diverse sample of 421 adolescents (57% girls; 14 to 19 years) completed measures of peer crowd affiliation, peer victimization, and qualities of best friendships and romantic relationships. Peer crowd affiliations (high and low status), positive qualities in best friendships, and the presence of a dating relationship protected adolescents against feelings of social anxiety, whereas relational victimization and negative interactions in best friendships predicted high social anxiety. In contrast, affiliation with a high-status peer crowd afforded some protection against depressive affect; however, relational victimization and negative qualities of best friendships and romantic relationships predicted depressive symptoms. Some moderating effects for ethnicity were observed. Findings indicate that multiple aspects of adolescents' social relations uniquely contribute to feelings of internal distress. Implications for research and preventive interventions are discussed.
Transforming Dependency Structures to Logical Forms for Semantic Parsing

The strongly typed syntax of grammar formalisms such as CCG, TAG, LFG and HPSG offers a synchronous framework for deriving syntactic structures and semantic logical forms. In contrast—partly due to the lack of a strong type system—dependency structures are easy to annotate and have become a widely used form of syntactic analysis for many languages. However, the lack of a type system makes a formal mechanism for deriving logical forms from dependency structures challenging. We address this by introducing a robust system based on the lambda calculus for deriving neo-Davidsonian logical forms from dependency trees. These logical forms are then used for semantic parsing of natural language to Freebase. Experiments on the Free917 and Web-Questions datasets show that our representation is superior to the original dependency trees and that it outperforms a CCG-based representation on this task. Compared to prior work, we obtain the strongest result to date on Free917 and competitive results on WebQuestions.
Random program generator for Java JIT compiler test system

Java has been used widely, from servers to cellular phones. In order to test those Java runtime systems, many test tools have been provided. However these tools are not enough for Java JIT compilers. They should be tested by various executable bytecode sequences. Most test tools don't cover such kinds of a combination test. Therefore we developed a random test program generator and a Java JIT compiler test system. This test system automatically: 1) generates Java class files which are random, executable and finite; 2) inserts codes that output execution-process-dependent results; 3) runs these on the target JIT compiler and other tested Java runtimes; 4) compares results. In this paper, we describe how to generate random test programs, and how to test a Java JIT compiler, and then we evaluate quality (coverage) of generated random test programs.
A survey of statistical user simulation techniques for reinforcement-learning of dialogue management strategies

Within the broad field of spoken dialogue systems, the application of machine-learning approaches to dialogue management strategy design is a rapidly growing research area. The main motivation is the hope of building systems that learn through trial-and-error interaction what constitutes a good dialogue strategy. Training of such systems could in theory be done using human users or using corpora of humancomputer dialogue, but in practice the typically vast space of possible dialogue states and strategies cannot be explored without the use of automatic user simulation tools. This requirement for training statistical dialogue models has created an interesting new application area for predictive statistical user modelling and a variety of different techniques for simulating user behaviour have been presented in the literature ranging from simple Markov Models to Bayesian Networks. The development of reliable user simulation tools is critical to further progress on automatic dialogue management design but it holds many challenges, some of which have been encountered in other areas of current research on statistical user modelling, such as the problem of “concept drift”, the problem of combining content-based and collaboration-based modelling techniques, and user model evaluation. The latter topic is of particular interest, because simulation-based learning is currently one of the few applications of statistical user modelling which employs both direct “accuracy-based” and indirect “utility-based” evaluation techniques. In this paper, we briefly summarize the role of the dialogue manager in a spoken dialogue system, give a short introduction to reinforcement-learning of dialogue management strategies and review the literature on user modelling for simulation-based strategy learning. We further describe recent work on user model evaluation and discuss some of the current research issues in simulation-based learning from a user modelling perspective. 2 J. SCHATZMANN ET AL.
Evolutionary Computation and Structural Design : a Survey of the State of the Art *

Evolutionary computation is emerging as a new engineering computational paradigm, which may significantly change the present structural design practice. For this reason, an extensive study of evolutionary computation in the context of structural design has been conducted in the Information Technology and Engineering School at George Mason University and its results are reported here. First, a general introduction to evolutionary computation is presented and recent developments in this field are briefly described. Next, the field of evolutionary design is introduced and its relevance to structural design is explained. Further, the issue of creativity/novelty is discussed and possible ways of achieving it during a structural design process are suggested. Current research progress in building engineering systems’ representations, one of the key issues in evolutionary design, is subsequently discussed. Next, recent developments in constraint-handling methods in evolutionary optimization are reported. Further, the rapidly growing field of evolutionary multiobjective optimization is presented and briefly described. An emerging subfield of coevolutionary design is subsequently introduced and its current advancements reported. Next, a comprehensive review of the applications of evolutionary computation in structural design is provided and chronologically classified. Finally, a summary of the current research status and a discussion on the most promising paths of future research are also presented.
Exploiting Workload Characteristics and Service Diversity to Improve the Availability of Cloud Storage Systems

With the increasing utilization and popularity of the cloud infrastructure, more and more data are moved to the cloud storage systems. This makes the availability of cloud storage services critically important, particularly given the fact that outages of cloud storage services have indeed happened from time to time. Thus, solely depending on a single cloud storage provider for storage services can risk violating the service-level agreement (SLA) due to the weakening of service availability. This has led to the notion of Cloud-of-Clouds, where data redundancy is introduced to distribute data among multiple independent cloud storage providers, to address the problem. The key in the effectiveness of the Cloud-of-Clouds approaches lies in how the data redundancy is incorporated and distributed among the clouds. However, the existing Cloud-of-Clouds approaches utilize either replication or erasure codes to redundantly distribute data across multiple clouds, thus incurring either high space or high performance overheads. In this paper, we propose a hybrid redundant data distribution approach, called HyRD, to improve the cloud storage availability in Cloud-of-Clouds by exploiting the workload characteristics and the diversity of cloud providers. In HyRD, large files are distributed in multiple cost-efficient cloud storage providers with erasure-coded data redundancy while small files and file system metadata are replicated on multiple high-performance cloud storage providers. The experiments conducted on our lightweight prototype implementation of HyRD show that HyRD improves the cost efficiency by 33.4 and 20.4 percent, and reduces the access latency by 58.7 and 34.8 percent than the DuraCloud and RACS schemes, respectively.
Visualizing Social Networks

With today‘s ubiquity and popularity of social network applications, the ability to analyze and understand large networks in an ef cient manner becomes critically important. However, as networks become larger and more complex, reasoning about social dynamics via simple statistics is not a feasible option. To overcome these limitations, we can rely on visual metaphors. Visualization nowadays is no longer a passive process that produces images from a set of numbers. Recent years have witnessed a convergence of social network analytics and visualization, coupled with interaction, that is changing the way analysts understand and characterize social networks. In this chapter, we discuss the main goal of visualization and how different metaphors are aimed towards elucidating different aspects of social networks, such as structure and semantics. We also describe a number of methods where analytics and visualization are interwoven towards providing a better comprehension of social structure and dynamics.
Development of an ultra wideband ground penetrating radar (UWB GPR) for nondestructive testing of underground objects

A compact ground penetrating radar (GPR) using an impulse waveform is developed. The UWB GPR system is composed of an impulse generator, ultra wideband transmitting and receiving antennas, high speed digitizer, and a laptop computer for data storage and image processing. By considering the maximum measuring depth and soil properties, a proper impulse generator is made and then a novel compact traveling wave monopole antenna is designed to transmit the impulse and receive the reflected signal. Also, image processing for visualization of buried targets is performed. For verification, a UWB GPR prototype is built and tested. It is shown that the system has very good depth and spatial resolution.
Stream reasoning : A survey and outlook A summary of ten years of research and a vision for the next decade

Stream reasoning studies the application of inference techniques to data characterised by being highly dynamic. It can find application in several settings, from Smart Cities to Industry 4.0, from Internet of Things to Social Media analytics. This year stream reasoning turns ten, and in this article we analyse its growth. In the first part, we trace the main results obtained so far, by presenting the most prominent studies. We start by an overview of the most relevant studies developed in the context of semantic web, and then we extend the analysis to include contributions from adjacent areas, such as database and artificial intelligence. Looking at the past is useful to prepare for the future: in the second part, we present a set of open challenges and issues that stream reasoning will face in the next future.
CLAMI: Defect Prediction on Unlabeled Datasets

Defect prediction on new projects or projects with limited historical data is an interesting problem in defect prediction studies. This is largely because it is difficult to collect defect information to label a dataset for training a prediction model. Cross-project defect prediction (CPDP) has tried to solve this problem by reusing prediction models built by other projects that have enough historical data. However, CPDP does not always build a strong prediction model because of the different distributions among datasets. Approaches for defect prediction on unlabeled datasets have also tried to address the problem by adopting unsupervised learning but it has one major limitation, the necessity for manual effort. In this study, we propose novel approaches, CLA and CLAMI, that show the potential for defect prediction on unlabeled datasets in an automated manner without need for manual effort. The key idea of the CLA and CLAMI approaches is to label an unlabeled dataset by using the magnitude of metric values. In our empirical study on seven open-source projects, the CLAMI approach led to the promising prediction performances, 0.636 and 0.723 in average f-measure and AUC, that are comparable to those of defect prediction based on supervised learning.
Feature Selection for High-Dimensional Data - A Pearson Redundancy Based Filter

An algorithm for filtering information based on the Pearson χ test approach has been implemented and tested on feature selection. This test is frequently used in biomedical data analysis and should be used only for nominal (discretiz ed) features. This algorithm has only one parameter, statistical confidence level that two di stributions are identical. Empirical comparisons with four other state-of-the-art features selection algorithms (FCBF, CorrSF, ReliefF and ConnSF) are very encouraging.
Evaluation of health information systems - problems and challenges

Information technology (IT) is emerging in health care. A rigorous evaluation of this technology is recommended and of high importance for decision makers and users. However, many authors report problems during the evaluation of information technology in health care. In this paper, we discuss some of these problems, and propose possible solutions for these problems.



Based on own experience and backed up by a literature review, some important problems during IT evaluation in health care together with their reasons, consequences and possible solutions are presented and structured.



We define three main problem areas-the complexity of the evaluation object, the complexity of an evaluation project, and the motivation for evaluation. Many evaluation problems can be subsumed under those three problem areas. A broadly accepted framework for evaluation of IT in healthcare seems desirable to address those problems. Such a framework should help to formulate relevant questions, to find adequate methods and tools, and to apply them in a sensible way.
Improved Semantic Segmentation for Histopathology using Rotation Equivariant Convolutional Networks

We propose a semantic segmentation model for histopathology that exploits rotation and reflection symmetries inherent in histopathology images. We demonstrate significant performance gains due to increased weight sharing, as well as improvements in predictive stability. The group-equivariant CNN framework is extended for segmentation by introducing a new (G → Z)-convolution that transforms feature maps on a group to planar feature maps. Also, equivariant transposed convolution is formulated for up-sampling in an encoder-decoder network. We further show the importance of exploiting more symmetries by varying the size of the group.
Small Solutions to Polynomial Equations, and Low Exponent RSA Vulnerabilities

We show how to find sufficiently small integer solutions to a polynomial in a single variable modulo N, and to a polynomial in two variables over the integers. The methods sometimes extend to more variables. As applications: RSA encryption with exponent 3 is vulnerable if the opponent knows two-thirds of the message, or if two messages agree over eight-ninths of their length; and we can find the factors of N=PQ if we are given the high order $\frac{1}{4} \log_2 N$ bits of P.
DEVELOPMENT OF A NOVEL UWB VIVALDI ANTENNA ARRAY USING SIW TECHNOLOGY

A compact Vivaldi antenna array printed on thick substrate and fed by a Substrate Integrated Waveguides (SIW) structure has been developed. The antenna array utilizes a compact SIW binary divider to significantly minimize the feed structure insertion losses. The low-loss SIW binary divider has a common novel Grounded Coplanar Waveguide (GCPW) feed to provide a wideband transition to the SIW and to sustain a good input match while preventing higher order modes excitation. The antenna array was designed, fabricated, and thoroughly investigated. Detailed simulations of the antenna and its feed, in addition to its relevant measurements, will be presented in this paper.
Cortical thickness in untreated transsexuals.

Sex differences in cortical thickness (CTh) have been extensively investigated but as yet there are no reports on CTh in transsexuals. Our aim was to determine whether the CTh pattern in transsexuals before hormonal treatment follows their biological sex or their gender identity. We performed brain magnetic resonance imaging on 94 subjects: 24 untreated female-to-male transsexuals (FtMs), 18 untreated male-to-female transsexuals (MtFs), and 29 male and 23 female controls in a 3-T TIM-TRIO Siemens scanner. T1-weighted images were analyzed to obtain CTh and volumetric subcortical measurements with FreeSurfer software. CTh maps showed control females have thicker cortex than control males in the frontal and parietal regions. In contrast, males have greater right putamen volume. FtMs had a similar CTh to control females and greater CTh than males in the parietal and temporal cortices. FtMs had larger right putamen than females but did not differ from males. MtFs did not differ in CTh from female controls but had greater CTh than control males in the orbitofrontal, insular, and medial occipital regions. In conclusion, FtMs showed evidence of subcortical gray matter masculinization, while MtFs showed evidence of CTh feminization. In both types of transsexuals, the differences with respect to their biological sex are located in the right hemisphere.
Super-Resolution Using Sub-Band Self-Similarity

A popular approach for single image super-resolution (SR) is to use scaled down versions of the given image to build an internal training dictionary of pairs of low resolution (LR) and high resolution (HR) image patches, which is then used to predict the HR image. This self-similarity approach has the advantage of not requiring a separate external training database. However, due to their limited size, internal dictionaries are often inadequate for finding good matches for patches containing complex structures such as textures. Furthermore, the quality of matches found are quite sensitive to factors like patch size (larger patches contain structures of greater complexity and may be difficult to match), and dimensions of the given image (smaller images yield smaller internal dictionaries). In this paper we propose a self-similarity based SR algorithm that addresses the abovementioned drawbacks. Instead of seeking similar patches directly in the image domain, we use the selfsimilarity principle independently on each of a set of different sub-band images, obtained using a bank of orientation selective band-pass filters. Therefore, we allow the different directional frequency components of a patch to find matches independently, which may be in different image locations. Essentially, we decompose local image structure into component patches defined by different sub-bands, with the following advantages: (1) The sub-band image patches are simpler and therefore easier to find matches, than for the more complex textural patches from the original image. (2) The size of the dictionary defined by patches from the sub-band images is exponential in the number of sub-bands used, thus increasing the effective size of the internal dictionary. (3) As a result, our algorithm exhibits a greater degree of invariance to parameters like patch size and the dimensions of the LR image. We demonstrate these advantages and show that our results are richer in textural content and appear more natural than several state-of-the-art methods.
A Review of Power Harvesting from Vibration using Piezoelectric Materials

The process of acquiring the energy surrounding a system and converting it into usable electrical energy is termed power harvesting. In the last few years, there has been a surge of research in the area of power harvesting. This increase in research has been brought on by the modern advances in wireless technology and low-power electronics such as microelectromechanical systems. The advances have allowed numerous doors to open for power harvesting systems in practical real-world applications. The use of piezoelectric materials to capitalize on the ambient vibrations surrounding a system is one method that has seen a dramatic rise in use for power harvesting. Piezoelectric materials have a crystalline structure that provides them with the ability to transform mechanical strain energy into electrical charge and, vice versa, to convert an applied electrical potential into mechanical strain. This property provides these materials with the ability to absorb mechanical energy from their surroundings, usually ambient vibration, and transform it into electrical energy that can be used to power other devices. While piezoelectric materials are the major method of harvesting energy, other methods do exist; for example, one of the conventional methods is the use of electromagnetic devices. In this paper we discuss the research that has been performed in the area of power harvesting and the future goals that must be achieved for power harvesting systems to find their way into everyday use.