nils-herrmann/clean_scirepeval huggingface dataset:
Query Graphs, Implementing Trees, and Freely-Reorderable Outerjoins

We determine when a join/outerjoin query can be expressed unambiguously as a query graph, without an explicit specification of the order of evaluation. To do so, we first characterize the set of expression trees that implement a given join/outerjoin query graph, and investigate the existence of transformations among the various trees. Our main theorem is that a join/outerjoin query is freely reorderable if the query graph derived from it falls within a particular class, every tree that “implements” such a graph evaluates to the same result.
The result has applications to language design and query optimization. Languages that generate queries within such a class do not require the user to indicate priority among join operations, and hence may present a simplified syntax. And it is unnecessary to add extensive analyses to a conventional query optimizer in order to generate legal reorderings for a freely-reorderable language.
The capacity for music: What is it, and what’s special about it?

We explore the capacity for music in terms of five questions: (1) What cognitive structures are invoked by music? (2) What are the principles that create these structures? (3) How do listeners acquire these principles? (4) What pre-existing resources make such acquisition possible? (5) Which aspects of these resources are specific to music, and which are more general? We examine these issues by looking at the major components of musical organization: rhythm (an interaction of grouping and meter), tonal organization (the structure of melody and harmony), and affect (the interaction of music with emotion). Each domain reveals a combination of cognitively general phenomena, such as gestalt grouping principles, harmonic roughness, and stream segregation, with phenomena that appear special to music and language, such as metrical organization. These are subtly interwoven with a residue of components that are devoted specifically to music, such as the structure of tonal systems and the contours of melodic tension and relaxation that depend on tonality. In the domain of affect, these components are especially tangled, involving the interaction of such varied factors as general-purpose aesthetic framing, communication of affect by tone of voice, and the musically specific way that tonal pitch contours evoke patterns of posture and gesture.
Content-Based Collaborative Filtering for News Topic Recommendation

News recommendation has become a big attraction with which major Web search portals retain their users. Contentbased Filtering and Collaborative Filtering are two effective methods, each serving a specific recommendation scenario. The Content-based Filtering approaches inspect rich contexts of the recommended items, while the Collaborative Filtering approaches predict the interests of long-tail users by collaboratively learning from interests of related users. We have observed empirically that, for the problem of news topic displaying, both the rich context of news topics and the long-tail users exist. Therefore, in this paper, we propose a Content-based Collaborative Filtering approach (CCF) to bring both Content-based Filtering and Collaborative Filtering approaches together. We found that combining the two is not an easy task, but the benefits of CCF are impressive. On one hand, CCF makes recommendations based on the rich contexts of the news. On the other hand, CCF collaboratively analyzes the scarce feedbacks from the long-tail users. We tailored this CCF approach for the news topic displaying on the Bing front page and demonstrated great gains in attracting users. In the experiments and analyses part of this paper, we discuss the performance gains and insights in news topic recommendation in Bing.
Chapter 7. Motion Tracking Requirements and Technologies 7.1. Introduction 7.1.1. Taxonomy of Applications for Motion Tracking

The science of motion tracking is fascinating because of its highly interdisciplinary nature and wide range of applications. This chapter will attempt to capture the interdisciplinary approach by organizing the subject differently from the several excellent review articles already available (Meyer et al, 1992; Ferrin, 1991; Bhatnagar, 1993; National Research Council, 1995). These reviews tend to break trackers into several technology categories, and evaluate the merits of each technology by inferring from commonalties amongst the performance and ergonomics of existing trackers in that class. This survey will instead focus on what capabilities are required for various applications, and what methods can be used to realize these capabilities.
Full-Duplex Communication in Cognitive Radio Networks: A Survey

Wireless networks with their ubiquitous applications have become an indispensable part of our daily lives. Wireless networks demand more and more spectral resources to support the ever increasing numbers of users. According to network engineers, the current spectrum crunch can be addressed with the introduction of cognitive radio networks (CRNs). In half-duplex (HD) CRNs, the secondary users (SUs) can either only sense the spectrum or transmit at a given time. This HD operation limits the SU throughput, because the SUs cannot transmit during the spectrum sensing. However, with the advances in self-interference suppression (SIS), full-duplex (FD) CRNs allow for simultaneous spectrum sensing and transmission on a given channel. This FD operation increases the throughput and reduces collisions as compared with HD-CRNs. In this paper, we present a comprehensive survey of FD-CRN communications. We cover the supporting network architectures and the various transmit and receive antenna designs. We classify the different SIS approaches in FD-CRNs. We survey the spectrum sensing approaches and security requirements for FD-CRNs. We also survey major advances in FD medium access control protocols as well as open issues, challenges, and future research directions to support the FD operation in CRNs.
Performance Modeling and System Management for Multi-component Online Services

Many dynamic-content online services are comprised of multiple interacting components and data partitions distributed across server clusters. Understanding the performance of these services is crucial for efficient system management. This paper presents a profile-driven performance model for cluster-based multi-component online services. Our offline constructed application profiles characterize component resource needs and inter-component communications. With a given component placement strategy, the application profile can be used to predict system throughput and average response time for the online service. Our model differentiates remote invocations from fast-path calls between co-located components and we measure the network delay caused by blocking inter-component communications. Validation with two J2EE-based online applications show that our model can predict application performance with small errors (less than 13% for throughput and less than 14% for the average response time). We also explore how this performance model can be used to assist system management functions for multi-component online services, with case examinations on optimized component placement, capacity planning, and cost-effectiveness analysis.
Adaptive rate stream processing for smart grid applications on clouds

Pervasive smart meters that continuously measure power usage by consumers within a smart (power) grid are providing utilities and power systems researchers with unprecedented volumes of information through streams that need to be processed and analyzed in near realtime. We introduce the use of Cloud platforms to perform scalable, latency sensitive stream processing for eEngineering applications in the smart grid domain. One unique aspect of our work is the use of adaptive rate control to throttle the rate of generation of power events by smart meters, which meets accuracy requirements of smart grid applications while consuming 50% lesser bandwidth resources in the Cloud.
Approximation and Estimation Bounds for Artificial Neural Networks

For a common class of artificial neural networks, the mean integrated squared error between the estimated network and a target function f is shown to be bounded by $${\text{O}}\left( {\frac{{C_f^2 }}{n}} \right) + O(\frac{{ND}}{N}\log N)$$ where n is the number of nodes, d is the input dimension of the function, N is the number of training observations, and C f is the first absolute moment of the Fourier magnitude distribution of f. The two contributions to this total risk are the approximation error and the estimation error. Approximation error refers to the distance between the target function and the closest neural network function of a given architecture and estimation error refers to the distance between this ideal network function and an estimated network function. With n ~ C f(N/(dlog N))1/2 nodes, the order of the bound on the mean integrated squared error is optimized to be O(C f((d/N)log N)1/2). The bound demonstrates surprisingly favorable properties of network estimation compared to traditional series and nonparametric curve estimation techniques in the case that d is moderately large. Similar bounds are obtained when the number of nodes n is not preselected as a function of C f (which is generally not known a priori), but rather the number of nodes is optimized from the observed data by the use of a complexity regularization or minimum description length criterion. The analysis involves Fourier techniques for the approximation error, metric entropy considerations for the estimation error, and a calculation of the index of resolvability of minimum complexity estimation of the family of networks.
Activity Recall in Visual Cortical Ensemble

Cue-triggered recall of learned temporal sequences is an important cognitive function that has been attributed to higher brain areas. Here recordings in both anesthetized and awake rats demonstrate that after repeated stimulation with a moving spot that evoked sequential firing of an ensemble of primary visual cortex (V1) neurons, just a brief flash at the starting point of the motion path was sufficient to evoke a sequential firing pattern that reproduced the activation order evoked by the moving spot. The speed of recalled spike sequences may reflect the internal dynamics of the network rather than the motion speed. In awake rats, such recall was observed during a synchronized ('quiet wakeful') brain state having large-amplitude, low-frequency local field potential (LFP) but not in a desynchronized ('active') state having low-amplitude, high-frequency LFP. Such conditioning-enhanced, cue-evoked sequential spiking of a V1 ensemble may contribute to experience-based perceptual inference in a brain state–dependent manner.
Measuring diabetes self-care: a psychometric analysis of the Self-Care Inventory-Revised with adults.

To examine psychometric properties of the Self-Care Inventory-revised (SCI-R), a self-report measure of perceived adherence to diabetes self-care recommendations, among adults with diabetes.



We used three data sets of adult type 1 and type 2 diabetic patients to examine psychometric properties of the SCI-R. Principal component and factor analyses examined whether a general factor or common factors were present. Associations with measures of theoretically related concepts were examined to assess SCI-R concurrent and convergent validity. Internal reliability coefficients were calculated. Responsiveness was assessed using paired t tests, effect size, and Guyatt's statistic for type 1 patients who completed psychoeducation.



Principal component and factor analyses identified a general factor but no consistent common factors. Internal consistency of the SCI-R was alpha = 0.87. Correlation with a measure of frequency of diabetes self-care behaviors was r = 0.63, providing evidence for SCI-R concurrent validity. The SCI-R correlated with diabetes-related distress (r = -0.36), self-esteem (r = 0.25), self-efficacy (r = 0.47), depression (r = -0.22), anxiety (r = -0.24), and HbA(1c) (r = -0.37), supporting construct validity. Responsiveness analyses showed SCI-R scores improved with diabetes psychoeducation with a medium effect size of 0.62 and a Guyatt's statistic of 0.85.



The SCI-R is a brief, psychometrically sound measure of perceptions of adherence to recommended diabetes self-care behaviors of adults with type 1 or type 2 diabetes.
Music genre classification using LBP textural features

In this paper we present an approach to music genre classification which converts an audio signal into spectrograms and extracts texture features from these time-frequency images which are then used for modeling music genres in a classification system. The texture features are based on Local Binary Pattern, a structural texture operator that has been successful in recent image classification research. Experiments are performed with two well-known datasets: the Latin Music Database (LMD), and the ISMIR 2004 dataset. The proposed approach takes into account some different zoning mechanisms to perform local feature extraction. Results obtained with and without local feature extraction are compared. We compare the performance of texture features with that of commonly used audio content based features (i.e. from the MARSYAS framework), and show that texture features always outperforms the audio content based features. We also compare our results with results from the literature. On the LMD, the performance of our approach reaches about 82.33%, above the best result obtained in the MIREX 2010 competition on that dataset. On the ISMIR 2004 database, the best result obtained is about 80.65%, i.e. below the best result on that dataset found in the literature. & 2012 Elsevier B.V. All rights reserved.
Design and acquisition of a telephone spontaneous speech dialogue corpus in Spanish: DIHANA

In the framework of the DIHANA project, we present the acquisition process of a spontaneous speech dialogue corpus in Spanish. The selected application consists of information retrieval by telephone for nationwide trains. A total of 900 dialogues from 225 users were acquired using theWizard of Oztechnique. In this work, we present the design and planning of the dialogue scenes and the wizard strategy used for the acquisition of the corpus. Then, we also present the acquisition tools and a description of the acquisition process.
Power losses and efficiency analysis of multilevel dc-dc converters

This paper presents a detailed analysis about the power losses and efficiency of multilevel dc-dc converters. The analysis considers different loss mechanisms and gives out quantitative descriptions of the power losses and useful design criteria. The analysis is based on a three-level multilevel dc-dc converter and can be extended to other switched-capacitor converters. The comparison between the theoretical analysis and the experimental results are shown to substantiate the theory
High-Speed Generator and Multilevel Converter for Energy Recovery in Automotive Systems

This paper deals with the design solution adopted for a high-speed axial-flux permanent-magnet generator devoted to supplementing power in automotive 42 V electrical systems through a low-voltage multilevel neutral-point-clamped converter. The axial-flux permanent-magnet generator is suitably designed to be directly coupled with a radial turbo-expander which provides recovery of kinetic energy available from the exhaust gases of an internal-combustion engine. The multilevel converter is properly sized for systems having low voltage and high first-order harmonic frequency. The paper describes the proposed 4-kW power rating generating system and discusses various issues resulting from electromagnetic, thermal, and mechanical design of the high-speed axial-flux permanent-magnet generator. As well, investigations on modulation strategy, total harmonic distortion, and power losses are presented for the low-voltage neutral-point-clamped power electronic converter.
Equality of Opportunity in Supervised Learning

We propose a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features. Assuming data about the predictor, target, and membership in the protected group are available, we show how to optimally adjust any learned predictor so as to remove discrimination according to our definition. Our framework also improves incentives by shifting the cost of poor classification from disadvantaged groups to the decision maker, who can respond by improving the classification accuracy. In line with other studies, our notion is oblivious: it depends only on the joint statistics of the predictor, the target and the protected attribute, but not on interpretation of individual features. We study the inherent limits of defining and identifying biases based on such oblivious measures, outlining what can and cannot be inferred from different oblivious tests. We illustrate our notion using a case study of FICO credit scores.
Industrial Applications of the Kalman Filter: A Review

The Kalman filter (KF) has received a huge interest from the industrial electronics community and has played a key role in many engineering fields since the 1970s, ranging, without being exhaustive, trajectory estimation, state and parameter estimation for control or diagnosis, data merging, signal processing, and so on. This paper provides a brief overview of the industrial applications and implementation issues of the KF in six topics of the industrial electronics community, highlighting some relevant reference papers and giving future research trends.
TraPlan: An Effective Three-in-One Trajectory-Prediction Model in Transportation Networks

The existing approaches for trajectory prediction (TP) are primarily concerned with discovering frequent trajectory patterns (FTPs) from historical movement data. Moreover, most of these approaches work by using a linear TP model to depict the positions of objects, which does not lend itself to the complexities of most real-world applications. In this research, we propose a three-in-one TP model in road-constrained transportation networks called TraPlan. TraPlan contains three essential techniques: 1) constrained network R-tree (CNR-tree), which is a two-tiered dynamic index structure of moving objects based on transportation networks; 2) a region-of-interest (RoI) discovery algorithm is employed to partition a large number of trajectory points into distinct clusters; and 3) a FTP-tree-based TP approach, called FTP-mining, is proposed to discover FTPs to infer future locations of objects moving within RoIs. In order to evaluate the results of the proposed CNR-tree index structure, we conducted experiments on synthetically generated data sets taken from real-world transportation networks. The results show that the CNR-tree can reduce the time cost of index maintenance by an average gap of about 40% when compared with the traditional NDTR-tree, as well as reduce the time cost of trajectory queries. Moreover, compared with fixed network R-Tree (FNR-trees), the accuracy of range queries has shown an on average improvement of about 32%. Furthermore, the experimental results show that the TraPlan demonstrates accurate and efficient prediction of possible motion curves of objects in distinct trajectory data sets by over 80% on average. Finally, we evaluate these results and the performance of the TraPlan model in regard to TP by comparing it with other TP algorithms.
Robust Euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm

The problem of geometric alignment of two roughly pre-registered, partially overlapping, rigid, noisy 3D point sets is considered. A new natural and simple, robustified extension of the popular Iterative Closest Point (ICP) algorithm  is presented, called Trimmed ICP. The new algorithm is based on the consistent use of the Least Trimmed Squares approach in all phases of the operation. Convergence is proved and an efficient implementation is discussed. TrICP is fast, applicable to overlaps under 50%, robust to erroneous and incomplete measurements, and has easy-to-set parameters. ICP is a special case of TrICP when the overlap parameter is 100%. Results of a performance evaluation study on the SQUID database of 1100 shapes are presented. The tests compare TrICP and the Iterative Closest Reciprocal Point algorithm .
Information-Flow Security for a Core of JavaScript

Tracking information flow in dynamic languages remains an important and intricate problem. This paper makes substantial headway toward understanding the main challenges and resolving them. We identify language constructs that constitute a core of Java Script: objects, higher-order functions, exceptions, and dynamic code evaluation. The core is powerful enough to naturally encode native constructs as arrays, as well as functionalities of Java Script's API from the document object model (DOM) related to document tree manipulation and event processing. As the main contribution, we develop a dynamic type system that guarantees information-flow security for this language.
Noisy Subspace Clustering via Matching Pursuits

Sparsity-based subspace clustering algorithms have attracted significant attention thanks to their excellent performance in practical applications. A prominent example is the sparse subspace clustering (SSC) algorithm by Elhamifar and Vidal, which performs spectral clustering based on an adjacency matrix obtained by sparsely representing each data point in terms of all the other data points via the Lasso. When the number of data points is large or the dimension of the ambient space is high, the computational complexity of SSC quickly becomes prohibitive. Dyer et al. observed that SSC-orthogonal matching pursuit (OMP) obtained by replacing the Lasso by the greedy OMP algorithm results in significantly lower computational complexity, while often yielding comparable performance. The central goal of this paper is an analytical performance characterization of SSC-OMP for noisy data. Moreover, we introduce and analyze the SSC-matching pursuit (MP) algorithm, which employs MP in lieu of OMP. Both SSC-OMP and SSC-MP are proven to succeed even when the subspaces intersect and when the data points are contaminated by severe noise. The clustering conditions we obtain for SSC-OMP and SSC-MP are similar to those for SSC and for the thresholding-based subspace clustering (TSC) algorithm due to Heckel and Bölcskei. Analytical results in combination with numerical results indicate that both SSC-OMP and SSC-MP with a data-dependent stopping criterion automatically detect the dimensions of the subspaces underlying the data. Experiments on synthetic and on real data show that SSC-MP often matches or exceeds the performance of the computationally more expensive SSC-OMP algorithm. Moreover, SSC-MP compares very favorably to SSC, TSC, and the nearest subspace neighbor algorithm, both in terms of clustering performance and running time. In addition, we find that, in contrast to SSC-OMP, the performance of SSC-MP is very robust with respect to the choice of parameters in the stopping criteria.