nils-herrmann/clean_scirepeval huggingface dataset:
Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularity

Modeling and predicting the popularity of online content is a significant problem for the practice of information dissemination, advertising, and consumption. Recent work analyzing massive datasets advances our understanding of popularity, but one major gap remains: To precisely quantify the relationship between the popularity of an online item and the external promotions it receives. This work supplies the missing link between exogenous inputs from public social media platforms, such as Twitter, and endogenous responses within the content platform, such as YouTube. We develop a novel mathematical model, the Hawkes intensity process, which can explain the complex popularity history of each video according to its type of content, network of diffusion, and sensitivity to promotion. Our model supplies a prototypical description of videos, called an endo-exo map. This map explains popularity as the result of an extrinsic factor – the amount of promotions from the outside world that the video receives, acting upon two intrinsic factors – sensitivity to promotion, and inherent virality. We use this model to forecast future popularity given promotions on a large 5-months feed of the most-tweeted videos, and found it to lower the average error by 28.6% from approaches based on popularity history. Finally, we can identify videos that have a high potential to become viral, as well as those for which promotions will have hardly any effect.
Cyber Scanning: A Comprehensive Survey

Cyber scanning refers to the task of probing enterprise networks or Internet wide services, searching for vulnerabilities or ways to infiltrate IT assets. This misdemeanor is often the primarily methodology that is adopted by attackers prior to launching a targeted cyber attack. Hence, it is of paramount importance to research and adopt methods for the detection and attribution of cyber scanning. Nevertheless, with the surge of complex offered services from one side and the proliferation of hackers' refined, advanced, and sophisticated techniques from the other side, the task of containing cyber scanning poses serious issues and challenges. Furthermore recently, there has been a flourishing of a cyber phenomenon dubbed as cyber scanning campaigns - scanning techniques that are highly distributed, possess composite stealth capabilities and high coordination - rendering almost all current detection techniques unfeasible. This paper presents a comprehensive survey of the entire cyber scanning topic. It categorizes cyber scanning by elaborating on its nature, strategies and approaches. It also provides the reader with a classification and an exhaustive review of its techniques. Moreover, it offers a taxonomy of the current literature by focusing on distributed cyber scanning detection methods. To tackle cyber scanning campaigns, this paper uniquely reports on the analysis of two recent cyber scanning incidents. Finally, several concluding remarks are discussed.
JADE: a FIPA2000 compliant agent development environment

JADE (Java Agent Development Framework) is a software environment to build agent systems for the management of networked information resources in compliance with the FIPA2000 specifications for interoperable intelligent multi- agent systems. The goal of JADE is to simplify development while ensuring standard compliance through a comprehensive set of system services and agents. JADE offers an agent runtime system on which implement efficient FIPA 2000 compliant multi- agent systems and supports their development through the availability of a predefined programmable agent model and of a set of management and testing tools. This paper describes the main features of the JADE system and introduces some of the most important projects based on JADE software.
Globally Coherent Text Generation with Neural Checklist Models

Recurrent neural networks can generate locally coherent text but often have difficulties representing what has already been generated and what still needs to be said – especially when constructing long texts. We present the neural checklist model, a recurrent neural network that models global coherence by storing and updating an agenda of text strings which should be mentioned somewhere in the output. The model generates output by dynamically adjusting the interpolation among a language model and a pair of attention models that encourage references to agenda items. Evaluations on cooking recipes and dialogue system responses demonstrate high coherence with greatly improved semantic coverage of the agenda.
New kids on the block: an analysis of modern blockchains

Half a decade after Bitcoin became the first widely used cryptocurrency, blockchains are receiving considerable interest from industry and the research community. Modern blockchains feature services such as name registration and smart contracts. Some employ new forms of consensus, such as proof-of-stake instead of proof-of-work. However, these blockchains are so far relatively poorly investigated, despite the fact that they move considerable assets. In this paper, we explore three representative, modern blockchains—Ethereum, Namecoin, and Peercoin. Our focus is on the features that set them apart from the pure currency use case of Bitcoin. We investigate the blockchains’ activity in terms of transactions and usage patterns, identifying some curiosities in the process. For Ethereum, we are mostly interested in the smart contract functionality it offers. We also carry out a brief analysis of issues that are introduced by negligent design of smart contracts. In the case of Namecoin, our focus is how the name registration is used and has developed over time. For Peercoin, we are interested in the use of proofof-stake, as this consensus algorithm is poorly understood yet used to move considerable value. Finally, we relate the above to the fundamental characteristics of the underlying peer-to-peer networks. We present a crawler for Ethereum and give statistics on the network size. For Peercoin and Namecoin, we identify the relatively small size of the networks and the weak bootstrapping process.
Exploring STT-MRAM Based In-Memory Computing Paradigm with Application of Image Edge Extraction

In this paper, we propose a novel Spin-Transfer Torque Magnetic Random-Access Memory (STT-MRAM) array design that could simultaneously work as non-volatile memory and implement a reconfigure in-memory logic operation without add-on logic circuits to the memory chip. The computed output could be simply read out like a typical MRAM bit-cell through the modified peripheral circuit. Such intrinsic in-memory computation can be used to process data locally and transfers the "cooked" data to the primary processing unit (i.e. CPU or GPU) for complex computation with high precision requirement. It greatly reduces power-hungry and long distance data communication, and further leads to extreme parallelism within memory. In this work, we further propose an in-memory edge extraction algorithm as a case study to demonstrate the efficiency of in-memory preprocessing methodology. The simulation results show that our edge extraction method reduces data communication as much as 8x for grayscale image, thus greatly reducing system energy consumption. Meanwhile, the F-measure result shows only &#x223C;10% degradation compared to conventional edge detection operators, such as Prewitt, Sobel and Roberts.
From Frequency to Meaning: Vector Space Models of Semantics

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term–document, word–context, and pair–pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field.
Hierarchical Bayesian Matrix Factorization with Side Information

Bayesian treatment of matrix factorization has been successfully applied to the problem of collaborative prediction, where unknown ratings are determined by the predictive distribution, inferring posterior distributions over user and item factor matrices that are used to approximate the user-item matrix as their product. In practice, however, Bayesian matrix factorization suffers from cold-start problems, where inferences are required for users or items about which a sufficient number of ratings are not gathered. In this paper we present a method for Bayesian matrix factorization with side information, to handle cold-start problems. To this end, we place Gaussian-Wishart priors on mean vectors and precision matrices of Gaussian user and item factor matrices, such that mean of each prior distribution is regressed on corresponding side information. We develop variational inference algorithms to approximately compute posterior distributions over user and item factor matrices. In addition, we provide Bayesian Cramér-Rao Bound for our model, showing that the hierarchical Bayesian matrix factorization with side information improves the reconstruction over the standard Bayesian matrix factorization where the side information is not used. Experiments on MovieLens data demonstrate the useful behavior of our model in the case of cold-start problems.
An evaluation of Naive Bayesian anti-spam filtering

It has recently been argued that a Naive Bayesian classifier can be used to filter unsolicited bulk e-mail (“spam”). We conduct a thorough evaluation of this proposal on a corpus that we make publicly available, contributing towards standard benchmarks. At the same time we investigate the effect of attribute-set size, training-corpus size, lemmatization, and stop-lists on the filter’s performance, issues that had not been previously explored. After introducing appropriate cost-sensitive evaluation measures, we reach the conclusion that additional safety nets are needed for the Naive Bayesian anti-spam filter to be viable in practice.
Area and volume coherence for efficient visualization of 3D scalar functions

We present an algorithm for compositing a combination of density clouds and contour surfaces used to represent a scalar function on a 3-D volume subdivided into convex polyhedra. The scalar function is interpolated between values defined at the vertices, and the polyhedra are sorted in depth before compositing. For n tetrahedra comprising a Delaunay triangulation, this sorting can always be done in O(n) time. Since a Delaunay triangulation can be efficiently computed for scattered data points, this provides a method for visualizing such data sets. The integrals for opacity and visible intensity along a ray through a convex polyhedron are computed analytically, and this computation is coherent across the polyhedron's projected area.
A test collection for entity search in DBpedia

We develop and make publicly available an entity search test collection based on the DBpedia knowledge base. This includes a large number of queries and corresponding relevance judgments from previous benchmarking campaigns, covering a broad range of information needs, ranging from short keyword queries to natural language questions. Further, we present baseline results for this collection with a set of retrieval models based on language modeling and BM25. Finally, we perform an initial analysis to shed light on certain characteristics that make this data set particularly challenging.
Fitting B-spline curves to point clouds by curvature-based squared distance minimization

Computing a curve to approximate data points is a problem encountered frequently in many applications in computer graphics, computer vision, CAD/CAM, and image processing. We present a novel and efficient method, called squared distance minimization (SDM), for computing a planar B-spline curve, closed or open, to approximate a target shape defined by a point cloud, that is, a set of unorganized, possibly noisy data points. We show that SDM significantly outperforms other optimization methods used currently in common practice of curve fitting. In SDM, a B-spline curve starts from some properly specified initial shape and converges towards the target shape through iterative quadratic minimization of the fitting error. Our contribution is the introduction of a new fitting error term, called the squared distance (SD) error term, defined by a curvature-based quadratic approximant of squared distances from data points to a fitting curve. The SD error term faithfully measures the geometric distance between a fitting curve and a target shape, thus leading to faster and more stable convergence than the point distance (PD) error term, which is commonly used in computer graphics and CAGD, and the tangent distance (TD) error term, which is often adopted in the computer vision community. To provide a theoretical explanation of the superior performance of SDM, we formulate the B-spline curve fitting problem as a nonlinear least squares problem and conclude that SDM is a quasi-Newton method which employs a curvature-based positive definite approximant to the true Hessian of the objective function. Furthermore, we show that the method based on the TD error term is a Gauss-Newton iteration, which is unstable for target shapes with high curvature variations, whereas optimization based on the PD error term is the alternating method that is known to have linear convergence.
Recurrent Instance Segmentation

Instance segmentation is the problem of detecting and delineating each distinct object of interest appearing in an image. Current instance segmentation approaches consist of ensembles of modules that are trained independently of each other, thus missing opportunities for joint learning. Here we propose a new instance segmentation paradigm consisting in an end-to-end method that learns how to segment instances sequentially. The model is based on a recurrent neural network that sequentially finds objects and their segmentations one at a time. This net is provided with a spatial memory that keeps track of what pixels have been explained and allows occlusion handling. In order to train the model we designed a principled loss function that accurately represents the properties of the instance segmentation problem. In the experiments carried out, we found that our method outperforms recent approaches on multiple person segmentation, and all state of the art approaches on the Plant Phenotyping dataset for leaf counting.
AUMPNet: Simultaneous Action Units Detection and Intensity Estimation on Multipose Facial Images Using a Single Convolutional Neural Network

This paper presents an unified convolutional neural network (CNN), named AUMPNet, to perform both Action Units (AUs) detection and intensity estimation on facial images with multiple poses. Although there are a variety of methods in the literature designed for facial expression analysis, only few of them can handle head pose variations. Therefore, it is essential to develop new models to work on non-frontal face images, for instance, those obtained from unconstrained environments. In order to cope with problems raised by pose variations, an unique CNN, based on region and multitask learning, is proposed for both AU detection and intensity estimation tasks. Also, the available head pose information was added to the multitask loss as a constraint to the network optimization, pushing the network towards learning better representations. As opposed to current approaches that require ad hoc models for every single AU in each task, the proposed network simultaneously learns AU occurrence and intensity levels for all AUs. The AUMPNet was evaluated on an extended version of the BP4D-Spontaneous database, which was synthesized into nine different head poses and made available to FG 2017 Facial Expression Recognition and Analysis Challenge (FERA 2017) participants. The achieved results surpass the FERA 2017 baseline, using the challenge metrics, for AU detection by 0.054 in F1-score and 0.182 in ICC(3, 1) for intensity estimation.
Color-based object recognition

The purpose is to arrive at recognition of multicolored objects invariant to a substantial change in viewpoint, object geometry and illumination. Assuming dichromatic reflectance and white illumination, it is shown that normalized color rgb, saturation S and hue H, and the newly proposed color models c 1 c 2 c 3 and l 1 l 2 l 3 are all invariant to a change in viewing direction, object geometry and illumination. Further, it is shown that hue H and l 1 l 2 l 3 are also invariant to highlights. Finally, a change in spectral power distribution of the illumination is considered to propose a new color constant color model m 1 m 2 m 3 . To evaluate the recognition accuracy differentiated for the various color models, experiments have been carried out on a database consisting of 500 images taken from 3-D multicolored man-made objects. The experimental results show that highest object recognition accuracy is achieved by l 1 l 2 l 3 and hue H followed by c 1 c 2 c 3 , normalized color rgb and m 1 m 2 m 3 under the constraint of white illumination. Also, it is demonstrated that recognition accuracy degrades substantially for all color features other than m 1 m 2 m 3 with a change in illumination color. The recognition scheme and images are available within the PicToSeek and Pic2Seek systems on-line at: http: //www.wins.uva.nl/research/isis/zomax/. ( 1999 Pattern Recognition Society.
An NMOS-LDO Regulated Switched-Capacitor DC–DC Converter With Fast-Response Adaptive-Phase Digital Control

A fully integrated low-dropout-regulated step-down multiphase-switched-capacitor DC-DC converter (a.k.a. charge pump, CP) with a fast-response adaptive-phase (Fast-RAP) digital controller is designed using a 65-nm CMOS process. Different from conventional designs, a low-dropout regulator (LDO) with an NMOS power stage is used without the need for an additional stepup CP for driving. A clock tripler and a pulse divider are proposed to enable the Fast-RAP control. As the Fast-RAP digital controller is designed to be able to respond faster than the cascaded linear regulator, transient response will not be affected by the adaptive scheme. Thus, light-load efficiency is improved without sacrificing the response time. When the CP operates at 90 MHz with 80.3% CP efficiency, only small ripples would appear on the CP output with the 18-phase interleaving scheme, and be further attenuated at VOUT by the 50-mV dropout regulator with only 4.1% efficiency overhead and 6.5% area overhead. The output ripple is less than 2 mV for a load current of 20 mA.
User profiling and behavioral adaptation for HRI: A survey

To effectively collaborate with people, robots are expected to detect and profile the users they are interacting with, but also to modify and adapt their behavior according to the learned models. The goal of this survey is to focus on the perspective of user profiling and behavioral adaptation. On the one hand, human-robot interaction requires a human-oriented perception to model and recognize the human actions and capabilities, the intentions and goals behind such actions, and the parameters characterizing the social interaction. On the other hand, the robot behavior should be adapted in its physical movement within the space, in the actions to be selected to achieve collaboration, and by modulating the parameters characterizing the interaction. In this direction, this survey of the current literature introduces a general classification scheme for both the profiling and the behavioral adaptation research topics in terms of physical, cognitive, and social interaction viewpoints.
A climbing robot based on Hot Melt Adhesion

Robust climbing in unstructured environment has been one of the long-standing challenges in robotics research. Among others, the control of large adhesion forces is still an important problem that significantly restricts the locomotion performance of climbing robots. The main contribution of this paper is to propose a novel approach to autonomous robot climbing which makes use of Hot Melt Adhesion (HMA). The HMA material is known as a very economical solution to achieve large adhesion forces, and the forces can be varied by controlling its material temperature. For locomotion in both inclined and vertical walls, this paper investigates the basic characteristics of HMA material, and proposes a design and control of climbing robot that uses the HMA material for attaching and detaching its body to the environment. The robot is equipped with servomotors and thermal control units to actively vary the temperature of the material, and the coordination of these components enables the robot to walk against the gravitational forces even with a relatively large body weight. A real-world platform is used to demonstrate locomotion on a vertical wall, and the experimental result explains feasibility and overall performances of this approach.
High-speed, miniaturized fluorescence microscopy in freely moving mice

A central goal in biomedicine is to explain organismic behavior in terms of causal cellular processes. However, concurrent observation of mammalian behavior and underlying cellular dynamics has been a longstanding challenge. We describe a miniaturized (1.1 g mass) epifluorescence microscope for cellular-level brain imaging in freely moving mice, and its application to imaging microcirculation and neuronal Ca2+ dynamics.
WIDEBAND GYSEL POWER DIVIDER WITH ARBITRARY POWER DIVISION BASED ON PATCH TYPE STRUCTURE

A novel Gysel power divider based on patch type structure is presented in this paper. The proposed power divider possesses broad bandwidth, small physical occupation and arbitrary power division. More than 30% bandwidth enhancement is achieved based on the −15 dB input return loss criteria, while 55% size reduction is realized compared with conventional Gysel power divider. What’s more, flat dividing is obtained in the design without using additional transmission line sections. Based on the novel structure, a design procedure of power dividers with unequal power division ratios is provided without using narrow microstrip line. To verify the design approach, the proposed power dividers with equal and unequal (2:1 and 4:1) power divisions at the centre frequency 1.5GHz are fabricated and measured. The results demonstrate that the design can fulfil our goals.
Local Spectral Graph Convolution for Point Set Feature Learning

Feature learning on point clouds has shown great promise, with the introduction of effective and generalizable deep learning frameworks such as pointnet++. Thus far, however, point features have been abstracted in an independent and isolated manner, ignoring the relative layout of neighboring points as well as their features. In the present article, we propose to overcome this limitation by using spectral graph convolution on a local graph, combined with a novel graph pooling strategy. In our approach, graph convolution is carried out on a nearest neighbor graph constructed from a point’s neighborhood, such that features are jointly learned. We replace the standard max pooling step with a recursive clustering and pooling strategy, devised to aggregate information from within clusters of nodes that are close to one another in their spectral coordinates, leading to richer overall feature descriptors. Through extensive experiments on diverse datasets, we show a consistent demonstrable advantage for the tasks of both point set classification and segmentation.