nils-herrmann/clean_scirepeval huggingface dataset:
LIBSVX: A Supervoxel Library and Benchmark for Early Video Processing

Supervoxel segmentation has strong potential to be incorporated into early video analysis as superpixel segmentation has in image analysis. However, there are many plausible supervoxel methods and little understanding as to when and where each is most appropriate. Indeed, we are not aware of a single comparative study on supervoxel segmentation. To that end, we study seven supervoxel algorithms, including both off-line and streaming methods, in the context of what we consider to be a good supervoxel: namely, spatiotemporal uniformity, object/region boundary detection, region compression and parsimony. For the evaluation we propose a comprehensive suite of seven quality metrics to measure these desirable supervoxel characteristics. In addition, we evaluate the methods in a supervoxel classification task as a proxy for subsequent high-level uses of the supervoxels in video analysis. We use six existing benchmark video datasets with a variety of content-types and dense human annotations. Our findings have led us to conclusive evidence that the hierarchical graph-based (GBH), segmentation by weighted aggregation (SWA) and temporal superpixels (TSP) methods are the top-performers among the seven methods. They all perform well in terms of segmentation accuracy, but vary in regard to the other desiderata: GBH captures object boundaries best; SWA has the best potential for region compression; and TSP achieves the best undersegmentation error.
Language within our grasp

In monkeys, the rostral part of ventral premotor cortex (area F5) contains neurons that discharge, both when the monkey grasps or manipulates objects and when it observes the experimenter making similar actions. These neurons (mirror neurons) appear to represent a system that matches observed events to similar, internally generated actions, and in this way forms a link between the observer and the actor. Transcranial magnetic stimulation and positron emission tomography (PET) experiments suggest that a mirror system for gesture recognition also exists in humans and includes Broca's area. We propose here that such an observation/execution matching system provides a necessary bridge from'doing' to'communicating',as the link between actor and observer becomes a link between the sender and the receiver of each message.
A Framework for Assessing the Global Diffusion of the Internet

This paper presents a comprehensive framework for describing the diffusion of the Internet in a country. It incorporates insights gained from in-depth studies of about 25 countries undertaken since 1997. The framework characterizes diffusion using six dimensions, defining them in detail, and examines how the six dimensions relate to underlying bodies of theory from the national systems of innovation and diffusion of innovations approaches. It addresses how to apply the framework in practice, highlighting Internet diffusion determinants. This framework is useful for business stakeholders wanting to make use of and invest in the Internet, for policy makers debating how to positively (or negatively) influence its use and development, and for researchers studying the large-scale diffusion of complex, interrelated technologies.
2 REINVENTING GROUNDED THEORY : SOME QUESTIONS ABOUT THEORY , GROUND AND DISCOVERY

Grounded theory‖s popularity persists after three decades of broad-ranging critique. We discuss here three problematic notions – “theory,” “ground” and “discovery” – which linger in the continuing use and development of grounded theory procedures. We argue that far from providing the epistemic security promised by grounded theory, these notions – embodied in continuing reinventions of grounded theory – constrain and distort qualitative inquiry. We argue that what is contrived is not in fact theory in any meaningful sense, that “ground” is a misnomer when talking about interpretation and that what ultimately materializes following grounded theory procedures is less like discovery and more akin to invention. The procedures admittedly provide signposts for qualitative inquirers, but educational researchers should be wary, for the significance of interpretation, narrative and reflection can be undermined in the procedures of grounded theory.
Relationship between hours spent on the Internet and Web 2.0 in Higher Education

This article analyses the relationship between the number of hours spent by university students on the Internet and their attitude, training, use, impact and perception of difficulties in Web 2.0 integration, as well as their knowledge and use of Web 2.0 tools in Higher Education. To this end, we used a Likert scale applied to a sample of 403 students from the University of Huelva (UHU), Spain. To test the hypothesis, we conducted a one-way ANOVA with post hoc comparisons. The results obtained partially confirmed the general hypothesis. The greatest differences were found in the factors of impact and use of Web 2.0 tools, whereas the least differences were found in the factors of attitude, training, difficulties and knowledge of Web 2.0 tools. El presente artículo analiza la relación existente entre el número de horas diarias que el alumnado universitario dedica a internet y la actitud, formación, uso, impacto, perceptción de dificultades de la integración de la web 2.0, así como el conocimiento y uso de herramientas de la web 2.0 en educación universitaria. Para ello hemos usado una escala tipo Likert aplicada a una muestra de 403 alumnos de la Universidad de Huelva. Para contrastar la hipótesis realizamos una ANOVA de un factor con comparaciones post hoc. Los resultados obtenidos permiten confirmar parcialmente la hipótesis general. Las diferencias mayores se observan en los factores de impacto y uso de herramientas de la web 2.0, mientras que las diferencias menores se observan en los factores de actitud, formation, dificultades y conocimiento de herramientas de la web 2.0.
IEEE 802 . 11 ad : Directional 60 GHz Communication for Multi-Gbps

With the ratification of the IEEE 802.11ad amendment to the 802.11 standard in December 2012, a major step has been taken to bring consumer wireless communication to the millimeter wave (mm-Wave) band. However, multi-Gbps throughput and small interference footprint come at the price of adverse signal propagation characteristics and require a fundamental rethinking of Wi-Fi communication principles. This paper describes the design assumptions taken into consideration for the IEEE 802.11ad standard and the novel techniques defined to overcome the challenges of mm-Wave communication. In particular we study the transition from omni-directional to highly directional communication and its impact on the design of
Discontinuous-Current Mode Operation of a Two-Phase Interleaved Boost DC–DC Converter With Coupled Inductor

A two-phase interleaved boost dc–dc converter with an inversely coupled inductor in a discontinuous-current mode (DCM) is analyzed by the equivalent inductance method. Coupling effects on the circuit statuses are described, and a forced conduction of the power diode or reverse-paralleled diode of MOSFET is caused by coupling. As a result, three major circuit statuses are figured out according to the physical relationship between the input–output voltage ratio and the coupling coefficient, and their condition boundaries are used to classify the DCM operation modes. Then, considering the load (duty cycle) variation, ten DCM operation modes are comprehensively analyzed. The analysis can be used to make an easy prediction of operation modes, and extended to the analysis of an interleaved buck converter or buck/boost bidirectional converter with a coupled inductor in DCM. At last, a 300-W prototype is built and tested in the lab to verify the analysis.
Automated scalable segmentation of neurons from multispectral images

Reconstruction of neuroanatomy is a fundamental problem in neuroscience. Stochastic expression of colors in individual cells is a promising tool, although its use in the nervous system has been limited due to various sources of variability in expression. Moreover, the intermingled anatomy of neuronal trees is challenging for existing segmentation algorithms. Here, we propose a method to automate the segmentation of neurons in such (potentially pseudo-colored) images. The method uses spatio-color relations between the voxels, generates supervoxels to reduce the problem size by four orders of magnitude before the final segmentation, and is parallelizable over the supervoxels. To quantify performance and gain insight, we generate simulated images, where the noise level and characteristics, the density of expression, and the number of fluorophore types are variable. We also present segmentations of real Brainbow images of the mouse hippocampus, which reveal many of the dendritic segments.
The Seventh PASCAL Recognizing Textual Entailment Challenge

This paper presents the Seventh Recognizing Textual Entailment (RTE-7) challenge. This year’s challenge replicated the exercise proposed in RTE-6, consisting of a Main Task, in which Textual Entailment is performed on a real corpus in the Update Summarization scenario; a Main subtask aimed at detecting novel information; and a KBP Validation Task, in which RTE systems had to validate the output of systems participating in the KBP Slot Filling Task. Thirteen teams participated in the Main Task (submitting 33 runs) and 5 in the Novelty Detection Subtask (submitting 13 runs). The KBP Validation Task was undertaken by 2 participants which submitted 5 runs. The ablation test experiment, introduced in RTE-5 to evaluate the impact of knowledge resources used by the systems participating in the Main Task and extended also to tools in RTE-6, was also repeated in RTE-7.
Striving for Simplicity: The All Convolutional Net

Most modern convolutional neural networks (CNNs) used for object recognition are built using the same principles: Alternating convolution and max-pooling layers followed by a small number of fully connected layers. We re-evaluate the state of the art for object recognition from small images with convolutional networks, questioning the necessity of different components in the pipeline. We find that max-pooling can simply be replaced by a convolutional layer with increased stride without loss in accuracy on several image recognition benchmarks. Following this finding – and building on other recent work for finding simple network structures – we propose a new architecture that consists solely of convolutional layers and yields competitive or state of the art performance on several object recognition datasets (CIFAR-10, CIFAR-100, ImageNet). To analyze the network we introduce a new variant of the “deconvolution approach” for visualizing features learned by CNNs, which can be applied to a broader range of network structures than existing approaches.
Blind Source Separation in nonminimum-phase systems based on filter decomposition

This paper focuses on the causality problem in the task of Blind Source Separation (BSS) of speech signals in nonminimum-phase mixing channels. We propose a new algorithm for solving this problem using filter decomposition approach. Our proposed algorithm uses an integrated cost function in which independence criterion is defined in frequency-domain. The parameters of demixing system are derived in time-domain, so the algorithm has the benefits of both time and frequency-domain approaches. Compared to the previous work in this framework, our proposed algorithm is the extension of filter decomposition idea in multi-channel blind deconvolution to the problem of blind source separation of speech signals. The proposed method is capable of dealing with both minimum-phase and nonminimum-phase mixing situations. Simulation results show considerable improvement in separating speech signals specially when the mixing system is nonminimum-phase.
Church: a language for generative models

Formal languages for probabilistic modeling enable re-use, modularity, and descriptive clarity, and can foster generic inference techniques. We introduce Church, a universal language for describing stochastic generative processes. Church is based on the Lisp model of lambda calculus, containing a pure Lisp as its deterministic subset. The semantics of Church is defined in terms of evaluation histories and conditional distributions on such histories. Church also includes a novel language construct, the stochastic memoizer, which enables simple description of many complex non-parametric models. We illustrate language features through several examples, including: a generalized Bayes net in which parameters cluster over trials, infinite PCFGs, planning by inference, and various non-parametric clustering models. Finally, we show how to implement query on any Church program, exactly and approximately, using Monte Carlo techniques.
Is dark silicon useful? Harnessing the four horsemen of the coming dark silicon apocalypse

Due to the breakdown of Dennardian scaling, the percentage of a silicon chip that can switch at full frequency is dropping exponentially with each process generation. This utilization wall forces designers to ensure that, at any point in time, large fractions of their chips are effectively dark or dim silicon, i.e., either idle or significantly underclocked.
 As exponentially larger fractions of a chip's transistors become dark, silicon area becomes an exponentially cheaper resource relative to power and energy consumption. This shift is driving a new class of architectural techniques that "spend" area to "buy" energy efficiency. All of these techniques seek to introduce new forms of heterogeneity into the computational stack. We envision that ultimately we will see widespread use of specialized architectures that leverage these techniques in order to attain orders-of-magnitude improvements in energy efficiency.
 However, many of these approaches also suffer from massive increases in complexity. As a result, we will need to look towards developing pervasively specialized architectures that insulate the hardware designer and the programmer from the underlying complexity of such systems. In this paper, I discuss four key approaches--the four horsemen--that have emerged as top contenders for thriving in the dark silicon age. Each class carries with its virtues deep-seated restrictions that requires a careful understanding of the underlying tradeoffs and benefits.
Variational Inference with Normalizing Flows

The choice of approximate posterior distribution is one of the core problems in variational inference. Most applications of variational inference employ simple families of posterior approximations in order to allow for efficient inference, focusing on mean-field or other simple structured approximations. This restriction has a significant impact on the quality of inferences made using variational methods. We introduce a new approach for specifying flexible, arbitrarily complex and scalable approximate posterior distributions. Our approximations are distributions constructed through a normalizing flow, whereby a simple initial density is transformed into a more complex one by applying a sequence of invertible transformations until a desired level of complexity is attained. We use this view of normalizing flows to develop categories of finite and infinitesimal flows and provide a unified view of approaches for constructing rich posterior approximations. We demonstrate that the theoretical advantages of having posteriors that better match the true posterior, combined with the scalability of amortized variational approaches, provides a clear improvement in performance and applicability of variational inference.
Breaking the News: First Impressions Matter on Online News

A growing number of people are changing the way they consume news, replacing the traditional physical newspapers and magazines by their virtual online versions or/and weblogs. The interactivity and immediacy present in online news are changing the way news are being produced and exposed by media corporations. News websites have to create effective strategies to catch people’s attention and attract their clicks. In this paper we investigate possible strategies used by online news corporations in the design of their news headlines. We analyze the content of 69,907 headlines produced by four major global media corporations during a minimum of eight consecutive months in 2014. In order to discover strategies that could be used to attract clicks, we extracted features from the text of the news headlines related to the sentiment polarity of the headline. We discovered that the sentiment of the headline is strongly related to the popularity of the news and also with the dynamics of the posted comments on that particular news.
Recovery for overloaded mobile edge computing

Mobile edge computing (MEC) allows the use of its services with low latency, location awareness and mobility support to make up for the disadvantages of cloud computing. But an overloaded MEC system or any MEC system failure significantly degrades quality of experience (QoE) and negates the advantages of MEC. In this paper, we propose two different recovery schemes for overloaded or broken MEC. One recovery scheme is where an overloaded MEC offloads its work to available neighboring MECs within transfer range. The other recovery scheme is for situations when there is no available neighboring MEC within transfer range. The second scheme accesses user devices of a neighboring MEC adjacent to the overloaded MEC as ad-hoc relay nodes in order to bridge the transfer disconnect between two MECs. Numerical experiments demonstrated the performance and verified the possibility of the proposed schemes, providing insightful results for potential future
BCube: a high performance, server-centric network architecture for modular data centers

This paper presents BCube, a new network architecture specifically designed for shipping-container based, modular data centers. At the core of the BCube architecture is its server-centric network structure, where servers with multiple network ports connect to multiple layers of COTS (commodity off-the-shelf) mini-switches. Servers act as not only end hosts, but also relay nodes for each other. BCube supports various bandwidth-intensive applications by speeding-up one-to-one, one-to-several, and one-to-all traffic patterns, and by providing high network capacity for all-to-all traffic.
 BCube exhibits graceful performance degradation as the server and/or switch failure rate increases. This property is of special importance for shipping-container data centers, since once the container is sealed and operational, it becomes very difficult to repair or replace its components.
 Our implementation experiences show that BCube can be seamlessly integrated with the TCP/IP protocol stack and BCube packet forwarding can be efficiently implemented in both hardware and software. Experiments in our testbed demonstrate that BCube is fault tolerant and load balancing and it significantly accelerates representative bandwidth-intensive applications.
Autonomous Robot Navigation in Highly Populated Pedestrian Zones

In the past, there has been a tremendous progress in the area of autonomous robot navigation and a large variety of robots have been developed who demonstrated robust navigation capabilities indoors, in non-urban outdoor environments, or on roads and relatively few approaches focus on navigation in urban environments such as city centers. Urban areas, however, introduce numerous challenges for autonomous robots as they are rather unstructured and dynamic. In this paper, we present a navigation system for mobile robots designed to operate in crowded city environments and pedestrian zones. We describe the different components of this system including a SLAM module for dealing with huge maps of city centers, a planning component for inferring feasible paths taking also into account the traversability and type of terrain, a module for accurate localization in dynamic environments, and means for calibrating and monitoring the platform. Our navigation system has been implemented and tested in several large-scale field tests, in which a real robot autonomously navigated over several kilometers in a complex urban environment. This also included a public demonstration, during which the robot autonomously traveled along a more than three kilometer long route through the city center of Freiburg, Germany.
Latent Space Model for Road Networks to Predict Time-Varying Traffic

Real-time traffic prediction from high-fidelity spatiotemporal traffic sensor datasets is an important problem for intelligent transportation systems and sustainability. However, it is challenging due to the complex topological dependencies and high dynamism associated with changing road conditions. In this paper, we propose a Latent Space Model for Road Networks (LSM-RN) to address these challenges holistically. In particular, given a series of road network snapshots, we learn the attributes of vertices in latent spaces which capture both topological and temporal properties. As these latent attributes are time-dependent, they can estimate how traffic patterns form and evolve. In addition, we present an incremental online algorithm which sequentially and adaptively learns the latent attributes from the temporal graph changes. Our framework enables real-time traffic prediction by 1) exploiting real-time sensor readings to adjust/update the existing latent spaces, and 2) training as data arrives and making predictions on-the-fly. By conducting extensive experiments with a large volume of real-world traffic sensor data, we demonstrate the superiority of our framework for real-time traffic prediction on large road networks over competitors as well as baseline graph-based LSM's.
Contextualized Sarcasm Detection on Twitter

Sarcasm requires some shared knowledge between speaker and audience; it is a profoundly contextual phenomenon. Most computational approaches to sarcasm detection, however, treat it as a purely linguistic matter, using information such as lexical cues and their corresponding sentiment as predictive features. We show that by including extra-linguistic information from the context of an utterance on Twitter – such as properties of the author, the audience and the immediate communicative environment – we are able to achieve gains in accuracy compared to purely linguistic features in the detection of this complex phenomenon, while also shedding light on features of interpersonal interaction that enable sarcasm in conversation.