nils-herrmann/clean_scirepeval huggingface dataset:
Early detection of promoted campaigns on social media

Social media expose millions of users every day to information campaigns - some emerging organically from grassroots activity, others sustained by advertising or other coordinated efforts. These campaigns contribute to the shaping of collective opinions. While most information campaigns are benign, some may be deployed for nefarious purposes, including terrorist propaganda, political astroturf, and financial market manipulation. It is therefore important to be able to detect whether a meme is being artificially promoted at the very moment it becomes wildly popular. This problem has important social implications and poses numerous technical challenges. As a first step, here we focus on discriminating between trending memes that are either organic or promoted by means of advertisement. The classification is not trivial: ads cause bursts of attention that can be easily mistaken for those of organic trends. We designed a machine learning framework to classify memes that have been labeled as trending on Twitter. After trending, we can rely on a large volume of activity data. Early detection, occurring immediately at trending time, is a more challenging problem due to the minimal volume of activity data that is available prior to trending. Our supervised learning framework exploits hundreds of time-varying features to capture changing network and diffusion patterns, content and sentiment information, timing signals, and user meta-data. We explore different methods for encoding feature time series. Using millions of tweets containing trending hashtags, we achieve 75% AUC score for early detection, increasing to above 95% after trending. We evaluate the robustness of the algorithms by introducing random temporal shifts on the trend time series. Feature selection analysis reveals that content cues provide consistently useful signals; user features are more informative for early detection, while network and timing features are more helpful once more data is available.
Fuzzy control of a double inverted pendulum based on information fusion

Inverted pendulum system is a typical rapid, multivariable, nonlinear, absolute instability and non-minimum phase system, and it is a favorite problem in the field of control theory and application. In its control, the current main control method includes in fuzzy control, variable structure control and robust control etc. For fuzzy control of a double inverted pendulum, the research is focused on how to solve the “rule explosion” problem. The model and characteristics of the system are detailed analyzed; a status fusion function is designed using information fusion. By using it, the output variables of the system with six dimensions is synthesized as two variables: error and variation of error. From the fuzzy control theory, we also design the fuzzy controller of the double inverted pendulum system in MATLAB, and carried out the system simulation in Simulink, results show that the method is feasible.
Houdini: Fooling Deep Structured Prediction Models

Generating adversarial examples is a critical step for evaluating and improving the robustness of learning machines. So far, most existing methods only work for classification and are not designed to alter the true performance measure of the problem at hand. We introduce a novel flexible approach named Houdini for generating adversarial examples specifically tailored for the final performance measure of the task considered, be it combinatorial and non-decomposable. We successfully apply Houdini to a range of applications such as speech recognition, pose estimation and semantic segmentation. In all cases, the attacks based on Houdini achieve higher success rate than those based on the traditional surrogates used to train the models while using a less perceptible adversarial perturbation.
The Yo-Yo intermittent recovery test : a useful tool for evaluation of physical performance in intermittent sports.

The two Yo-Yo intermittent recovery (IR) tests evaluate an individual's ability to repeatedly perform intense exercise. The Yo-Yo IR level 1 (Yo-Yo IR1) test focuses on the capacity to carry out intermittent exercise leading to a maximal activation of the aerobic system, whereas Yo-Yo IR level 2 (Yo-Yo IR2) determines an individual's ability to recover from repeated exercise with a high contribution from the anaerobic system. Evaluations of elite athletes in various sports involving intermittent exercise showed that the higher the level of competition the better an athlete performs in the Yo-Yo IR tests. Performance in the Yo-Yo IR tests for young athletes increases with rising age. The Yo-Yo IR tests have shown to be a more sensitive measure of changes in performance than maximum oxygen uptake. The Yo-Yo IR tests provide a simple and valid way to obtain important information of an individual's capacity to perform repeated intense exercise and to examine changes in performance.
Fast RGB-D people tracking for service robots

Service robots have to robustly follow and interact with humans. In this paper, we propose a very fast multi-people tracking algorithm designed to be applied on mobile service robots. Our approach exploits RGB-D data and can run in real-time at very high frame rate on a standard laptop without the need for a GPU implementation. It also features a novel depthbased sub-clustering method which allows to detect people within groups or even standing near walls. Moreover, for limiting drifts and track ID switches, an online learning appearance classifier is proposed featuring a three-term joint likelihood. We compared the performances of our system with a number of state-of-the-art tracking algorithms on two public datasets acquired with three static Kinects and a moving stereo pair, respectively. In order to validate the 3D accuracy of our system, we created a new dataset in which RGB-D data are acquired by a moving robot. We made publicly available this dataset which is not only annotated by hand, but the ground-truth position of people and robot are acquired with a motion capture system in order to evaluate tracking accuracy and precision in 3D coordinates. Results of experiments on these datasets are presented, showing that, even without the need for a GPU, our approach achieves state-of-the-art accuracy and superior speed. Matteo Munaro Via Gradenigo 6A, 35131 Padova, Italy Tel.: +39-049-8277831 E-mail: matteo.munaro@dei.unipd.it Emanuele Menegatti Via Gradenigo 6A, 35131 Padova, Italy Tel.: +39-049-8277651 E-mail: emg@dei.unipd.it (a) (b) Fig. 1 Example of our system output: (a) a 3D bounding box is drawn for every tracked person on the RGB image, (b) the corresponding 3D point cloud is reported, together with the estimated people trajectories.
Network function virtualization in 5G

5G wireless technology is paving the way to revolutionize future ubiquitous and pervasive networking, wireless applications, and user quality of experience. To realize its potential, 5G must provide considerably higher network capacity, enable massive device connectivity with reduced latency and cost, and achieve considerable energy savings compared to existing wireless technologies. The main objective of this article is to explore the potential of NFV in enhancing 5G radio access networks' functional, architectural, and commercial viability, including increased automation, operational agility, and reduced capital expenditure.  Harnessing the potential of 5G and network functions virtualization, we discuss how NFV can address critical 5G design challenges through service abstraction and virtualized computing, storage, and network resources. We describe NFV implementation with network overlay and SDN technologies. In our discussion, we cover the first steps in understanding the role of NFV in implementing CoMP, D2D communication, and ultra densified networks.
Parametric Human Body Reconstruction Based on Sparse Key Points

We propose an automatic parametric human body reconstruction algorithm which can efficiently construct a model using a single Kinect sensor. A user needs to stand still in front of the sensor for a couple of seconds to measure the range data. The user's body shape and pose will then be automatically constructed in several seconds. Traditional methods optimize dense correspondences between range data and meshes. In contrast, our proposed scheme relies on sparse key points for the reconstruction. It employs regression to find the corresponding key points between the scanned range data and some annotated training data. We design two kinds of feature descriptors as well as corresponding regression stages to make the regression robust and accurate. Our scheme follows with dense refinement where a pre-factorization method is applied to improve the computational efficiency. Compared with other methods, our scheme achieves similar reconstruction accuracy but significantly reduces runtime.
FEEDING THE ILLUSION OF GROWTH AND HAPPINESS : A REPLY TO HAGERTY AND VEENHOVEN

If your letter had praised everything of mine, I would not have been as pleased as I am by your attempt to disprove and reject certain points. I regard this as a mark of friendship and the other as one of adulation. But in return I ask you to listen with an open mind to my rebuttal. For what you say, if it were allowed to pass without any reply from me, would be too one-sided. It is always a source of satisfaction to come across an article where one is cited often, especially by two scholars who have contributed so much to advance the study of subjective well-being. Of course, my happiness would have been greater had the references been favorable, rather than unfavorable. (Hereafter I use happiness and satisfaction interchangeably.) I take it that the Hagerty-Veenhoven (hereafter H-V) article (2003) is a rebuttal of my 1995 paper (Easterlin 1995), because there is only one reference to time series results of studies by other scholars done in the almost 10-year period since publication of my article. Indeed, I believe I detect an echo of a similar critique by one of the authors of my 1974 article (cf. Easterlin 1974 and Veenhoven 1991; for comments on the latter, see Easterlin 2004 forthcoming). 3 Apparently the editor and referee(s) of this Journal also viewed the H-V paper as a comment on my 1995 article; otherwise it would be hard to explain the absence of the customary literature review and reconciliation of new and disparate results with those of prior work. It seems appropriate, therefore, to offer a few comments in response, especially since the conclusions of the H-V article will no doubt be cited often as substantially different from my own when, in fact, they are not. I will focus on the time series analysis in the section " Descriptive Statistics of Happiness and Income " (pp. 11-18) which I take to be the heart of their article. Until one is sure about the data, methodology, and results of the time series analysis, hypothesis testing is superfluous. 1 THE UNITED STATES I was quite surprised to find the one country whose data I thought I knew fairly well to be among the seven for whom a significant positive correlation is reported between happiness and income. I had found no significant relationship between happiness and time over a period in which GDP …
Fast image segmentation based on K-Means clustering with histograms in HSV color space

A fast and efficient approach for color image segmentation is proposed. In this work, a new quantization technique for HSV color space is implemented to generate a color histogram and a gray histogram for K-Means clustering, which operates across different dimensions in HSV color space. Compared with the traditional K-Means clustering, the initialization of centroids and the number of cluster are automatically estimated in the proposed method. In addition, a filter for post-processing is introduced to effectively eliminate small spatial regions. Experiments show that the proposed segmentation algorithm achieves high computational speed, and salient regions of images can be effectively extracted. Moreover, the segmentation results are close to human perceptions.
Migrating from Virtualization to Dockerization in the Cloud: Simulation and Evaluation of Distributed Systems

Virtualization is the nucleus of the cloud computing for providing its services on-demand. Cloud-based distributed systems are predominantly developed using virtualization technology. However, the requirement of significant resources and issues of interoperability and deployment make it less adopt- able in the development of many types of distributed systems. Dockerization or Docker Container-based virtualization has been introduced in the last three years and gaining popularity in the software development community. Docker has recently introduced its distributed system development tool called Swarm, which extends the Docker Container-based system development process on multiple hosts in multiple clouds. Docker Swarm-based containerized distributed system is a brand new approach and needs to be compared with the virtualized distributed system. Therefore, this paper presents the simulation and evaluation of the development of a distributed system using virtualization and dockerization. This simulation is based on Docker Swarm, VirtualBox, Ubuntu, Mac OS X, nginx and redis. To simulate and evaluate the distributed system in the same environment, all Swarm Nodes and Virtual Machines are created using VirtualBox on the same Mac OS X host. For making this evaluation rational, almost similar system resources are allocated to both at the beginning. Subsequently, similar servers nginx and redis are installed on the Swarm Node and Virtual Machine. Finally, based on the experimental simulation results, it evaluates their required resources and operational overheads; thus, their performance and effectiveness for designing distributed systems.
Reconfigurable Battery Techniques and Systems: A Survey

Battery packs with a large number of battery cells are becoming more and more widely adopted in electronic systems, such as robotics, renewable energy systems, energy storage in smart grids, and electronic vehicles. Therefore, a well-designed battery pack is essential for battery applications. In the literature, the majority of research in battery pack design focuses on battery management system, safety circuit, and cell-balancing strategies. Recently, the reconfigurable battery pack design has gained increasing attentions as a promising solution to solve the problems existing in the conventional battery packs and associated battery management systems, such as low energy efficiency, short pack lifespan, safety issues, and low reliability. One of the most prominent features of reconfigurable battery packs is that the battery cell topology can be dynamically reconfigured in the real-time fashion based on the current condition (in terms of the state of charge and the state of health) of battery cells. So far, there are several reconfigurable battery schemes having been proposed and validated in the literature, all sharing the advantage of cell topology reconfiguration that ensures balanced cell states during charging and discharging, meanwhile providing strong fault tolerance ability. This survey is undertaken with the intent of identifying the state-of-the-art technologies of reconfigurable battery as well as providing review on related technologies and insight on future research in this emerging area.
Recurrent Inference Machines for Solving Inverse Problems

Much of the recent research on solving iterative inference problems focuses on moving away from hand-chosen inference algorithms and towards learned inference. In the latter, the inference process is unrolled in time and interpreted as a recurrent neural network (RNN) which allows for joint learning of model and inference parameters with back-propagation through time. In this framework, the RNN architecture is directly derived from a hand-chosen inference algorithm, effectively limiting its capabilities. We propose a learning framework, called Recurrent Inference Machines (RIM), in which we turn algorithm construction the other way round: Given data and a task, train an RNN to learn an inference algorithm. Because RNNs are Turing complete [1, 2] they are capable to implement any inference algorithm. The framework allows for an abstraction which removes the need for domain knowledge. We demonstrate in several image restoration experiments that this abstraction is effective, allowing us to achieve state-of-the-art performance on image denoising and super-resolution tasks and superior across-task generalization.
Dependency Parsing Techniques for Information Extraction

English. Dependency parsing is an important component in information extraction, in particular when using suitable formalisms and accurate and efficient parsing techniques. We review recent advances in dependency parsing and describe our own contribution in the context of the Evalita 2014 DPIE task. Italiano. Il parsing a dipendenze è un componente importante nell’estrazione di informazione da testi, in particolare quando usato con una rappresentazione adeguata e tecniche di parsing accurate ed efficienti. Accenniamo agli sviluppi recenti nel parsing a dipendenze e descriviamo il nostro contributo nel contesto del task DPIE di Evalita 2014.
Differentially Private High-Dimensional Data Publication via Sampling-Based Inference

Releasing high-dimensional data enables a wide spectrum of data mining tasks. Yet, individual privacy has been a major obstacle to data sharing. In this paper, we consider the problem of releasing high-dimensional data with differential privacy guarantees. We propose a novel solution to preserve the joint distribution of a high-dimensional dataset. We first develop a robust sampling-based framework to systematically explore the dependencies among all attributes and subsequently build a dependency graph. This framework is coupled with a generic threshold mechanism to significantly improve accuracy. We then identify a set of marginal tables from the dependency graph to approximate the joint distribution based on the solid inference foundation of the junction tree algorithm while minimizing the resultant error. We prove that selecting the optimal marginals with the goal of minimizing error is NP-hard and, thus, design an approximation algorithm using an integer programming relaxation and the constrained concave-convex procedure. Extensive experiments on real datasets demonstrate that our solution substantially outperforms the state-of-the-art competitors.
CNN+CNN: Convolutional Decoders for Image Captioning

Image captioning is a challenging task that combines the field of computer vision and natural language processing. A variety of approaches have been proposed to achieve the goal of automatically describing an image, and recurrent neural network (RNN) or long-short term memory (LSTM) based models dominate this field. However, RNNs or LSTMs cannot be calculated in parallel and ignore the underlying hierarchical structure of a sentence. In this paper, we propose a framework that only employs convolutional neural networks (CNNs) to generate captions. Owing to parallel computing, our basic model is around 3× faster than NIC (an LSTM-based model) during training time, while also providing better results. We conduct extensive experiments on MSCOCO and investigate the influence of the model width and depth. Compared with LSTM-based models that apply similar attention mechanisms, our proposed models achieves comparable scores of BLEU-1,2,3,4 and METEOR, and higher scores of CIDEr. We also test our model on the paragraph annotation dataset [22], and get higher CIDEr score compared with hierarchical LSTMs.
Representational Similarity Analysis – Connecting the Branches of Systems Neuroscience

A FUNDAMENTAL CHALLENGE FOR SYSTEMS NEUROSCIENCE IS TO QUANTITATIVELY RELATE ITS THREE MAJOR BRANCHES OF RESEARCH: brain-activity measurement, behavioral measurement, and computational modeling. Using measured brain-activity patterns to evaluate computational network models is complicated by the need to define the correspondency between the units of the model and the channels of the brain-activity data, e.g., single-cell recordings or voxels from functional magnetic resonance imaging (fMRI). Similar correspondency problems complicate relating activity patterns between different modalities of brain-activity measurement (e.g., fMRI and invasive or scalp electrophysiology), and between subjects and species. In order to bridge these divides, we suggest abstracting from the activity patterns themselves and computing representational dissimilarity matrices (RDMs), which characterize the information carried by a given representation in a brain or model. Building on a rich psychological and mathematical literature on similarity analysis, we propose a new experimental and data-analytical framework called representational similarity analysis (RSA), in which multi-channel measures of neural activity are quantitatively related to each other and to computational theory and behavior by comparing RDMs. We demonstrate RSA by relating representations of visual objects as measured with fMRI in early visual cortex and the fusiform face area to computational models spanning a wide range of complexities. The RDMs are simultaneously related via second-level application of multidimensional scaling and tested using randomization and bootstrap techniques. We discuss the broad potential of RSA, including novel approaches to experimental design, and argue that these ideas, which have deep roots in psychology and neuroscience, will allow the integrated quantitative analysis of data from all three branches, thus contributing to a more unified systems neuroscience.
A beaconless Opportunistic Routing based on a cross-layer approach for efficient video dissemination in mobile multimedia IoT applications

Mobile multimedia networks are enlarging the Internet of Things (IoT) portfolio with a huge number of multimedia services for different applications. Those services run on dynamic topologies due to device mobility or failures and wireless channel impairments, such as mobile robots or Unmanned Aerial Vehicle (UAV) environments for rescue or surveillance missions. In those scenarios, beaconless Opportunistic Routing (OR) allows increasing the robustness of systems for supporting routing decisions in a completely distributed manner. Moreover, the addition of a cross-layer scheme enhances the benefits of a beaconless OR, and also enables multimedia dissemination with Quality of Experience (QoE) support. However, existing beaconless OR approaches do not support a reliable and efficient cross-layer scheme to enable effective multimedia transmission under topology changes, increasing the packet loss rate, and thus reducing the video quality level based on the user’s experience. This article proposes a Link quality and Geographical beaconless OR protocol for efficient video dissemination for mobile multimedia IoT, called LinGO. This protocol relies on a beaconless OR approach and uses multiple metrics for routing decisions, including link quality, geographic location, and energy. A QoE/video-aware optimisation scheme allows increasing the packet delivery rate in presence of links errors, by adding redundant video packets based on the frame importance from the human’s point-of-view. Simulation results show that LinGO delivers live video flows with QoE support and robustness in mobile and dynamic topologies, as needed in future IoT environments.
Pke: an Open Source Python-based Keyphrase Extraction Toolkit

We describe pke, an open source python-based keyphrase extraction toolkit. It provides an end-to-end keyphrase extraction pipeline in which each component can be easily modified or extented to develop new approaches. pke also allows for easy benchmarking of state-of-the-art keyphrase extraction approaches, and ships with supervised models trained on the SemEval-2010 dataset (Kim et al., 2010).