nils-herrmann/clean_scirepeval huggingface dataset:
PGA: Using Graphs to Express and Automatically Reconcile Network Policies

Software Defined Networking (SDN) and cloud automation enable a large number of diverse parties (network operators, application admins, tenants/end-users) and control programs (SDN Apps, network services) to generate network policies independently and dynamically. Yet existing policy abstractions and frameworks do not support natural expression and automatic composition of high-level policies from diverse sources. We tackle the open problem of automatic, correct and fast composition of multiple independently specified network policies. We first develop a high-level Policy Graph Abstraction (PGA) that allows network policies to be expressed simply and independently, and leverage the graph structure to detect and resolve policy conflicts efficiently. Besides supporting ACL policies, PGA also models and composes service chaining policies, i.e., the sequence of middleboxes to be traversed, by merging multiple service chain requirements into conflict-free composed chains. Our system validation using a large enterprise network policy dataset demonstrates practical composition times even for very large inputs, with only sub-millisecond runtime latencies.
Framing , Agenda Setting , and Priming : The Evolution of Three Media Effects Models

This special issue of Journal of Communication is devoted to theoretical explanations of news framing, agenda setting, and priming effects. It examines if and how the three models are related and what potential relationships between them tell theorists and researchers about the effects of mass media. As an introduction to this effort, this essay provides a very brief review of the three effects and their roots in media-effects research. Based on this overview, we highlight a few key dimensions along which one can compare, framing, agenda setting, and priming. We conclude with a description of the contexts within which the three models operate, and the broader implications that these conceptual distinctions have for the growth of our discipline.
Segmentation Based Classification of 3D Urban Point Clouds: A Super-Voxel Based Approach with Evaluation

Segmentation and classification of urban range data into different object classes have several challenges due to certain properties of the data, such as density variation, inconsistencies due to missing data and the large data size that require heavy computation and large memory. A method to classify urban scenes based on a super-voxel segmentation of sparse 3D data obtained from LiDAR sensors is presented. The 3D point cloud is first segmented into voxels, which are then characterized by several attributes transforming them into super-voxels. These are joined together by using a link-chain method rather than the usual region growing algorithm to create objects. These objects are then classified using geometrical models and local descriptors. In order to evaluate the results, a new metric that combines both segmentation and classification results simultaneously is presented. The effects of voxel size and incorporation of RGB color and laser reflectance intensity on the classification results are also discussed. The method is evaluated on standard data sets using different metrics to demonstrate its efficacy.
Pixelating vector line art

Creating pixel art is a laborious process that requires artists to place individual pixels by hand. Although many image editors provide vector-to-raster conversions, the results produced do not meet the standards of pixel art: artifacts such as jaggies or broken lines frequently occur. We describe a novel Pixelation algorithm that rasterizes vector line art while adhering to established conventions used by pixel artists. We compare our results through a user study to those generated by Adobe Illustrator and Photoshop, as well as hand-drawn samples by both amateur and professional pixel artists.
Summing Up Hours of Any Type of Practice Versus Identifying Optimal Practice Activities: Commentary on Macnamara, Moreau, & Hambrick (2016).

In their original article, Ericsson, Krampe, and Tesch-Römer (1993) reviewed the evidence concerning the conditions of optimal learning and found that individualized practice with training tasks (selected by a supervising teacher) with a clear performance goal and immediate informative feedback was associated with marked improvement. We found that this type of deliberate practice was prevalent when advanced musicians practice alone and found its accumulated duration related to attained music performance. In contrast, Macnamara, Moreau, and Hambrick's (2016, this issue) main meta-analysis examines the use of the term deliberate practice to refer to a much broader and less defined concept including virtually any type of sport-specific activity, such as group activities, watching games on television, and even play and competitions. Summing up every hour of any type of practice during an individual's career implies that the impact of all types of practice activity on performance is equal-an assumption that I show is inconsistent with the evidence. Future research should collect objective measures of representative performance with a longitudinal description of all the changes in different aspects of the performance so that any proximal conditions of deliberate practice related to effective improvements can be identified and analyzed experimentally.
An efficient protocol for authenticated and secure message delivery in vehicular ad hoc networks

In Vehicular Ad Hoc Networks (VANETs), anonymity of the nodes sending messages should be preserved, while at the same time the law enforcement agencies should be able to trace the messages to the senders when necessary. It is also necessary that the messages sent are authenticated and delivered to the vehicles in the relevant areas quickly. In this paper, we present an efficient protocol for fast dissemination of authenticated messages in VANETs. It ensures the anonymity of the senders and also provides mechanism for law enforcement agencies to trace the messages to their senders, when necessary.
Hermes: Providing Tight Control over High-Performance SDN Switches

SDN controllers demand tight performance guarantees over the control plane actions performed by switches. For example, traffic engineering techniques that frequently reconfigure the network require guarantees on the speed of reconfiguring the network. Initial experiments show that poor performance of Ternary Content-Addressable Memory (TCAM) control actions (e.g., rule insertion) can inflate application performance by a factor of 2x! Yet, modern switches provide no guarantees for these important control plane actions -- inserting, modifying, or deleting rules.
 In this paper, we present the design and evaluation of Hermes, a practical and immediately deployable framework that offers a novel method for partitioning and optimizing switch TCAM to enable performance guarantees. Hermes builds on recent studies on switch performance and provides guarantees by trading-off a nominal amount of TCAM space for assured performance. We evaluated Hermes using large-scale simulations. Our evaluations show that with less than 5% overheads, Hermes provides 5ms insertion guarantees that translates into an improvement of application level metrics by up to 80%. Hermes is more than 50% better than existing state of the art techniques and provides significant improvement for traditional networks running BGP.
A survey of Web clustering engines

Web clustering engines organize search results by topic, thus offering a complementary view to the flat-ranked list returned by conventional search engines. In this survey, we discuss the issues that must be addressed in the development of a Web clustering engine, including acquisition and preprocessing of search results, their clustering and visualization. Search results clustering, the core of the system, has specific requirements that cannot be addressed by classical clustering algorithms. We emphasize the role played by the quality of the cluster labels as opposed to optimizing only the clustering structure. We highlight the main characteristics of a number of existing Web clustering engines and also discuss how to evaluate their retrieval performance. Some directions for future research are finally presented.
Optimization Methods for Convolutional Sparse Coding

Sparse and convolutional constraints form a natural prior for many optimization problems that arise from physical processes. Detecting motifs in speech and musical passages, super-resolving images, compressing videos, and reconstructing harmonic motions can all leverage redundancies introduced by convolution. Solving problems involving sparse and convolutional constraints remains a difficult computational problem, however. In this paper we present an overview of convolutional sparse coding in a consistent framework. The objective involves iteratively optimizing a convolutional least-squares term for the basis functions, followed by an L1-regularized least squares term for the sparse coefficients. We discuss a range of optimization methods for solving the convolutional sparse coding objective, and the properties that make each method suitable for different applications. In particular, we concentrate on computational complexity, speed to convergence, memory usage, and the effect of implied boundary conditions. We present a broad suite of examples covering different signal and application domains to illustrate the general applicability of convolutional sparse coding, and the efficacy of the available optimization methods.
An Efficient Randomized Algorithm for Detecting Circles

Detecting circles from a digital image is very important in shape recognition. In this paper, an efficient randomized algorithm (RCD) for detecting circles is presented, which is not based on the Hough transform (HT). Instead of using an accumulator for saving the information of the related parameters in the HT-based methods, the proposed RCD does not need an accumulator. The main concept used in the proposed RCD is that we first randomly select four edge pixels in the image and define a distance criterion to determine whether there is a possible circle in the image; after finding a possible circle, we apply an evidence-collecting process to further determine whether the possible circle is a true circle or not. Some synthetic images with different levels of noises and some realistic images containing circular objects with some occluded circles and missing edges have been taken to test the performance. Experimental results demonstrate that the proposed RCD is faster than other HT-based methods for the noise level between the light level and the modest level. For a heavy noise level, the randomized HT could be faster than the proposed RCD, but at the expense of massive memory requirements.c
Human resource information systems (HRIS) and technology trust

Scholars in many disciplines have considered the antecedents and consequences of various forms of trust. This paper generates 11 propositions exploring the relationship between Human Resource Information Systems (HRIS) and the trust an individual places in the inanimate technology (technology trust) and models the effect of those relationships on HRIS implementation success. Specifically, organizational, technological, and user factors are considered and modeled to generate a set of testable propositions that can subsequently be investigated in various organizational settings. Eleven propositions are offered suggesting that organizational trust, pooled interdependence, organizational community, organizational culture, technology adoption, technology utility, technology usability, socialization, sensitivity to privacy, and predisposition to trust influence an individual’s level of trust in the HRIS technology (technology trust) and ultimately the success of an HRIS implementation process. A summary of the relationships between the key constructs in the model and recommendations for future research are provided.
StreamQRE: modular specification and efficient evaluation of quantitative queries over streaming data

Real-time decision making in emerging IoT applications typically relies on computing quantitative summaries of large data streams in an efficient and incremental manner. To simplify the task of programming the desired logic, we propose StreamQRE, which provides natural and high-level constructs for processing streaming data. Our language has a novel integration of linguistic constructs from two distinct programming paradigms: streaming extensions of relational query languages and quantitative extensions of regular expressions. The former allows the programmer to employ relational constructs to partition the input data by keys and to integrate data streams from different sources, while the latter can be used to exploit the logical hierarchy in the input stream for modular specifications. 
 We first present the core language with a small set of combinators, formal semantics, and a decidable type system. We then show how to express a number of common patterns with illustrative examples. Our compilation algorithm translates the high-level query into a streaming algorithm with precise complexity bounds on per-item processing time and total memory footprint. We also show how to integrate approximation algorithms into our framework. We report on an implementation in Java, and evaluate it with respect to existing high-performance engines for processing streaming data. Our experimental evaluation shows that (1) StreamQRE allows more natural and succinct specification of queries compared to existing frameworks, (2) the throughput of our implementation is higher than comparable systems (for example, two-to-four times greater than RxJava), and (3) the approximation algorithms supported by our implementation can lead to substantial memory savings.
The balanced scorecard and IT governance

The balanced scorecard (BSC) initially developed by Kaplan and Norton, is a performance management system that should allow enterprises to drive their strategies on measurement and follow-up. In recent years the BSC has been applied to information technology (IT) and currently the first real-life IT applications are starting to emerge. In this article, it is shown how the IT balanced scorecard (IT BSC) can be linked to the business balanced scorecard (BU BSC) and in this way support the IT/business governance and alignment processes.
Towards the utilization of EEG as a brain imaging tool

Recent advances in signal analysis have engendered EEG with the status of a true brain mapping and brain imaging method capable of providing spatio-temporal information regarding brain (dys)function. Because of the increasing interest in the temporal dynamics of brain networks, and because of the straightforward compatibility of the EEG with other brain imaging techniques, EEG is increasingly used in the neuroimaging community. However, the full capability of EEG is highly underestimated. Many combined EEG-fMRI studies use the EEG only as a spike-counter or an oscilloscope. Many cognitive and clinical EEG studies use the EEG still in its traditional way and analyze grapho-elements at certain electrodes and latencies. We here show that this way of using the EEG is not only dangerous because it leads to misinterpretations, but it is also largely ignoring the spatial aspects of the signals. In fact, EEG primarily measures the electric potential field at the scalp surface in the same way as MEG measures the magnetic field. By properly sampling and correctly analyzing this electric field, EEG can provide reliable information about the neuronal activity in the brain and the temporal dynamics of this activity in the millisecond range. This review explains some of these analysis methods and illustrates their potential in clinical and experimental applications.
Improving the performance of Naive Bayes multinomial in e-mail foldering by introducing distribution-based balance of datasets

E-mail foldering or e-mail classification into user predefined folders can be viewed as a text classification/categorization problem. However, it has some intrinsic properties that make it more difficult to deal with, mainly the large cardinality of the class variable (i.e. the number of folders), the different number of e-mails per class state and the fact that this is a dynamic problem, in the sense that e-mails arrive in our mail-forders following a time-line. Perhaps because of these problems, standard text-oriented classifiers such as Naive Bayes Multinomial do no obtain a good accuracy when applied to e-mail corpora. In this paper, we identify the imbalance among classes/folders as the main problem, and propose a new method based on learning and sampling probability distributions. Our experiments over a standard corpus (ENRON) with seven datasets (e-mail users) show that the results obtained by Naive Bayes Multinomial significantly improve when applying the balancing algorithm first. For the sake of completeness in our experimental study we also compare this with another standard balancing method (SMOTE) and classifiers.
A broadband series power divider using zero-degree metamaterial phase-shifting lines

A metamaterial 1:4 series power divider that provides equal power split to all four output ports over a large bandwidth is presented, which can be extended to an arbitrary number of output ports. The divider comprises four nonradiating metamaterial lines in series, incurring a zero insertion phase over a large bandwidth, while simultaneously maintaining a compact length of /spl lambda//sub 0//8. Compared to a series power divider employing conventional one-wavelength long meandered transmission lines to provide in-phase signals at the output ports, the metamaterial divider provides a 165% increase in the input return-loss bandwidth and a 155% and 154% increase in the through-power bandwidth to ports 3 and 4, respectively. In addition, the metamaterial divider is significantly more compact, occupying only 2.6% of the area that the transmission line divider occupies. The metamaterial and transmission line dividers exhibit comparable insertion losses.
A Capacitive Rotary Encoder Based on Quadrature Modulation and Demodulation

This paper presents a capacitive rotary encoder for both angular position and angular speed measurements. The encoder is mainly composed of three parts: the transmitting segments; a pair of reflecting electrodes; and a pair of receiving electrodes. The transmitting segments together with four mutual quadrature carrier voltages provide a modulated electric field. The reflecting electrodes, which are patterned sinusoidally can encode the angular position to a phase/frequency modulated signal based on quadrature modulation. The modulated signal is then digitally decoded to the angular position in a field programmable gate array processor based on the quadrature demodulation and the coordinate rotational digital computer algorithm. Through a universal serial bus, the digital angular position is transmitted to a computer for further analysis in National Instruments' LabVIEW software. A prototype of the capacitive encoder shows that its precision is better than 0.006° and the resolution is 0.002°. The dynamic nonlinearity is evaluated at ±0.4° when the rotor is rotating at 1000 r/min.
ET Comes of Age: Envelope Tracking for Higher-Efficiency Power Amplifiers

Envelope tracking (ET) is by now a well-established technique that improves the efficiency of microwave power amplifiers (PAs) compared to what can be obtained with conventional class-AB or class-B operation for amplifying signals with a time-varying envelope, such as most of those used in present wireless communication systems. ET is poised to be deployed extensively in coming generations of amplifiers for cellular handsets because it can reduce power dissipation for signals using the long-term evolution (LTE) standard required for fourthgeneration (4G) wireless systems, which feature high peak-to-average power ratios (PAPRs). The ET technique continues to be actively developed for higher carrier frequencies and broader bandwidths. This article reviews the concepts and history of ET, discusses several applications currently on the drawing board, presents challenges for future development, and highlights some directions for improving the technique.
Towards Smart Farming and Sustainable Agriculture with Drones

The use of drones in agriculture is becoming more and more popular. The paper presents a novel approach to distinguish between different field's plowing techniques by means of an RGB-D sensor. The presented system can be easily integrated in commercially available Unmanned Aerial Vehicles (UAVs). In order to successfully classify the plowing techniques, two different measurement algorithms have been developed. Experimental tests show that the proposed methodology is able to provide a good classification of the field's plowing depths.
#nowplaying the Future Billboard: Mining Music Listening Behaviors of Twitter Users for Hit Song Prediction

Microblogs are rich sources of information because they provide platforms for users to share their thoughts, news, information, activities, and so on. Twitter is one of the most popular microblogs. Twitter users often use hashtags to mark specific topics and to link them with related tweets. In this study, we investigate the relationship between the music listening behaviors of Twitter users and a popular music ranking service by comparing information extracted from tweets with music-related hashtags and the Billboard chart. We collect users' music listening behavior from Twitter using music-related hashtags (e.g., #nowplaying). We then build a predictive model to forecast the Billboard rankings and hit music. The results show that the numbers of daily tweets about a specific song and artist can be effectively used to predict Billboard rankings and hits. This research suggests that users' music listening behavior on Twitter is highly correlated with general music trends and could play an important role in understanding consumers' music consumption patterns. In addition, we believe that Twitter users' music listening behavior can be applied in the field of Music Information Retrieval (MIR).
Fast Approximate Subgraph Counting and Enumeration

We present a new shared-memory parallel algorithm and implementation called FASCIA for the problems of approximate sub graph counting and sub graph enumeration. The problem of sub graph counting refers to determining the frequency of occurrence of a given sub graph (or template) within a large network. This is a key graph analytic with applications in various domains. In bioinformatics, sub graph counting is used to detect and characterize local structure (motifs) in protein interaction networks. Exhaustive enumeration and exact counting is extremely compute-intensive, with running time growing exponentially with the number of vertices in the template. In this work, we apply the color coding technique to determine approximate counts of non-induced occurrences of the sub graph in the original network. Color coding gives a fixed-parameter algorithm for this problem, using a dynamic programming-based counting approach. Our new contributions are a multilevel shared-memory parallelization of the counting scheme and several optimizations to reduce the memory footprint. We show that approximate counts can be obtained for templates with up to 12 vertices, on networks with up to millions of vertices and edges. Prior work on this problem has only considered out-of-core parallelization on distributed platforms. With our new counting scheme, data layout optimizations, and multicore parallelism, we demonstrate a significant speedup over the current state-of-the-art for sub graph counting.