nils-herrmann/clean_scirepeval huggingface dataset:
A Principled Way of Assessing Visualization Literacy

We describe a method for assessing the visualization literacy (VL) of a user. Assessing how well people understand visualizations has great value for research (e. g., to avoid confounds), for design (e. g., to best determine the capabilities of an audience), for teaching (e. g., to assess the level of new students), and for recruiting (e. g., to assess the level of interviewees). This paper proposes a method for assessing VL based on Item Response Theory. It describes the design and evaluation of two VL tests for line graphs, and presents the extension of the method to bar charts and scatterplots. Finally, it discusses the reimplementation of these tests for fast, effective, and scalable web-based use.
Experimental realization of quantum algorithm for solving linear systems of equations

"Experimental realization of quantum algorithm for solving linear systems of equations" (2014). Many important problems in science and engineering can be reduced to the problem of solving linear equations. The quantum algorithm discovered recently indicates that one can solve an N-dimensional linear equation in O(log N) time, which provides an exponential speedup over the classical counterpart. Here we report an experimental demonstration of the quantum algorithm when the scale of the linear equation is 2 × 2 using a nuclear magnetic resonance quantum information processor. For all sets of experiments, the fidelities of the final four-qubit states are all above 96%. This experiment gives the possibility of solving a series of practical problems related to linear systems of equations and can serve as the basis to realize many potential quantum algorithms.
Recurrent Chinese Restaurant Process with a Duration-based Discount for Event Identification from Twitter

Due to the fast development of social media on the Web, Twitter has become one of the major platforms for people to express themselves. Because of the wide adoption of Twitter, events like breaking news and release of popular videos can easily catch people’s attention and spread rapidly on Twitter, and the number of relevant tweets approximately reflects the impact of an event. Event identification and analysis on Twitter has thus become an important task. Recently the Recurrent Chinese Restaurant Process (RCRP) has been successfully used for event identification from news streams and news-centric social media streams. However, these models cannot be directly applied to Twitter based on our preliminary experiments mainly for two reasons: (1) Events emerge and die out fast on Twitter, while existing models ignore this burstiness property. (2) Most Twitter posts are personal interest oriented while only a small fraction is event related. Motivated by these challenges, we propose a new nonparametric model which considers burstiness. We further combine this model with traditional topic models to identify both events and topics simultaneously. Our quantitative evaluation provides sufficient evidence that our model can accurately detect meaningful events. Our qualitative evaluation also shows interesting analysis for events on Twitter.
Security and Privacy Issues in Deep Learning

Ho Bae†, Seoul National University, Republic of Korea Jaehee Jang†, Seoul National University, Republic of Korea Dahuin Jung, Seoul National University, Republic of Korea Hyemi Jang, Seoul National University, Republic of Korea Heonseok Ha, Seoul National University, Republic of Korea Sungroh Yoon*, Seoul National University, Republic of Korea *E-mail: sryoon@snu.ac.kr ∗: To whom correspondence should be addressed, †: These authors contributed equally to this work.
A unified Bayesian framework for MEG/EEG source imaging

The ill-posed nature of the MEG (or related EEG) source localization problem requires the incorporation of prior assumptions when choosing an appropriate solution out of an infinite set of candidates. Bayesian approaches are useful in this capacity because they allow these assumptions to be explicitly quantified using postulated prior distributions. However, the means by which these priors are chosen, as well as the estimation and inference procedures that are subsequently adopted to affect localization, have led to a daunting array of algorithms with seemingly very different properties and assumptions. From the vantage point of a simple Gaussian scale mixture model with flexible covariance components, this paper analyzes and extends several broad categories of Bayesian inference directly applicable to source localization including empirical Bayesian approaches, standard MAP estimation, and multiple variational Bayesian (VB) approximations. Theoretical properties related to convergence, global and local minima, and localization bias are analyzed and fast algorithms are derived that improve upon existing methods. This perspective leads to explicit connections between many established algorithms and suggests natural extensions for handling unknown dipole orientations, extended source configurations, correlated sources, temporal smoothness, and computational expediency. Specific imaging methods elucidated under this paradigm include the weighted minimum l(2)-norm, FOCUSS, minimum current estimation, VESTAL, sLORETA, restricted maximum likelihood, covariance component estimation, beamforming, variational Bayes, the Laplace approximation, and automatic relevance determination, as well as many others. Perhaps surprisingly, all of these methods can be formulated as particular cases of covariance component estimation using different concave regularization terms and optimization rules, making general theoretical analyses and algorithmic extensions/improvements particularly relevant.
HARPOON: An Obfuscation-Based SoC Design Methodology for Hardware Protection

Hardware intellectual-property (IP) cores have emerged as an integral part of modern system-on-chip (SoC) designs. However, IP vendors are facing major challenges to protect hardware IPs from IP piracy. This paper proposes a novel design methodology for hardware IP protection using netlist-level obfuscation. The proposed methodology can be integrated in the SoC design and manufacturing flow to simultaneously obfuscate and authenticate the design. Simulation results for a set of ISCAS-89 benchmark circuits and the advanced-encryption-standard IP core show that high levels of security can be achieved at less than 5% area and power overhead under delay constraint.
Neural 3D Mesh Renderer

For modeling the 3D world behind 2D images, which 3D representation is most appropriate? A polygon mesh is a promising candidate for its compactness and geometric properties. However, it is not straightforward to model a polygon mesh from 2D images using neural networks because the conversion from a mesh to an image, or rendering, involves a discrete operation called rasterization, which prevents back-propagation. Therefore, in this work, we propose an approximate gradient for rasterization that enables the integration of rendering into neural networks. Using this renderer, we perform single-image 3D mesh reconstruction with silhouette image supervision and our system outperforms the existing voxel-based approach. Additionally, we perform gradient-based 3D mesh editing operations, such as 2D-to-3D style transfer and 3D DeepDream, with 2D supervision for the first time. These applications demonstrate the potential of the integration of a mesh renderer into neural networks and the effectiveness of our proposed renderer.
Design and implementation of a brain-computer interface with high transfer rates

This paper presents a brain-computer interface (BCI) that can help users to input phone numbers. The system is based on the steady-state visual evoked potential (SSVEP). Twelve buttons illuminated at different rates were displayed on a computer monitor. The buttons constituted a virtual telephone keypad, representing the ten digits 0-9, BACKSPACE, and ENTER. Users could input phone number by gazing at these buttons. The frequency-coded SSVEP was used to judge which button the user desired. Eight of the thirteen subjects succeeded in ringing the mobile phone using the system. The average transfer rate over all subjects was 27.15 bits/min. The attractive features of the system are noninvasive signal recording, little training required for use, and high information transfer rate. Approaches to improve the performance of the system are discussed.
Mediated social touch: a review of current research and future directions

In this paper, we review research and applications in the area of mediated or remote social touch. Whereas current communication media rely predominately on vision and hearing, mediated social touch allows people to touch each other over a distance by means of haptic feedback technology. Overall, the reviewed applications have interesting potential, such as the communication of simple ideas (e.g., through Hapticons), establishing a feeling of connectedness between distant lovers, or the recovery from stress. However, the beneficial effects of mediated social touch are usually only assumed and have not yet been submitted to empirical scrutiny. Based on social psychological literature on touch, communication, and the effects of media, we assess the current research and design efforts and propose future directions for the field of mediated social touch.
Building a Data Collection for Deception Research

Research in high stakes deception has been held back by the sparsity of ground truth verification for data collected from real world sources. We describe a set of guidelines for acquiring and developing corpora that will enable researchers to build and test models of deceptive narrative while avoiding the problem of sanctioned lying that is typically required in a controlled experiment. Our proposals are drawn from our experience in obtaining data from court cases and other testimony, and uncovering the background information that enabled us to annotate claims made in the narratives as true or false.
Competing through service : Insights from service-dominant logic

Service-dominant logic (S-D logic) is contrasted with goods-dominant (G-D) logic to provide a framework for thinking more clearly about the concept of service and its role in exchange and competition. Then, relying upon the nine foundational premises of S-D logic [Vargo, Stephen L. and Robert F. Lusch (2004). â€œEvolving to a New Dominant Logic for Marketing, (January) 1â€“17; Lusch, Robert F. and Stephen L. Vargo (2006), â€œService-Dominant Logic as a Foundation for Building a General Theory,â€​ in The Service-Dominant Logic of Marketing: Dialog, Debate and Directions. Robert F. Lusch and Stephen L. Vargo (eds.), Armonk, NY: M.E. Sharpe, 406â€“420] nine derivative propositions are developed that inform marketers on how to compete through service. a c, 2 Purchase Export
Overview of sciDB: large scale array storage, processing and analysis

SciDB [4, 3] is a new open-source data management system intended primarily for use in application domains that involve very large (petabyte) scale array data; for example, scientific applications such as astronomy, remote sensing and climate modeling, bio-science information management, risk management systems in financial applications, and the analysis of web log data. In this talk we will describe our set of motivating examples and use them to explain the features of SciDB. We then briefly give an overview of the project 'in flight', explaining our novel storage manager, array data model, query language, and extensibility frameworks.
Building a Multi-tenant Cloud Service from Legacy Code with Docker Containers

In this paper we address the problem of migrating a legacy Web application to a cloud service. We develop a reusable architectural pattern to do so and validate it with a case study of the Beta release of the IBM Bluemix Workflow Service  (herein referred to as the Beta Workflow service). It uses Docker [2] containers and a Cloudant  persistence layer to deliver a multi-tenant cloud service by re-using a legacy codebase. We are not aware of any literature that addresses this problem by using containers.The Beta Workflow service provides a scalable, stateful, highly available engine to compose services with REST APIs. The composition is modeled as a graph but authored in a Javascript-based domain specific language that specifies a set of activities and control flow links among these activities. The primitive activities in the language can be used to respond to HTTP REST requests, invoke services with REST APIs, and execute Javascript code to, among other uses, extract and construct the data inputs and outputs to external services, and make calls to these services.Examples of workflows that have been built using the service include distributing surveys and coupons to customers of a retail store , the management of sales requests between a salesperson and their regional managers, managing the staged deployment of different versions of an application, and the coordinated transfer of jobs among case workers.
Spatio-Temporal Clustering : a Survey Spatio-Temporal Clustering : a Survey

Spatio-temporal clustering is a process of grouping objects based on their spatial and temporal similarity. It is relatively new subfield of data mining which gained high popularity especially in geographic information sciences due to the pervasiveness of all kinds of location-based or environmental devices that record position, time or/and environmental properties of an object or set of objects in realtime. As a consequence, different types and large amounts of spatio-temporal data became available that introduce new challenges to data analysis and require novel approaches to knowledge discovery. In this chapter we concentrate on the spatiotemporal clustering in geographic space. First, we provide a classification of different types of spatio-temporal data. Then, we focus on one type of spatio-temporal clustering trajectory clustering, pprovide an overview of the state-of-the-art approaches and methods of spatio-temporal clustering and finally present several scenarios in different application domains such as movement, cellular networks and environmental studies.
Transformerless Inverter With Virtual DC Bus Concept for Cost-Effective Grid-Connected PV Power Systems

In order to eliminate the common-mode (CM) leakage current in the transformerless photovoltaic (PV) systems, the concept of the virtual dc bus is proposed in this paper. By connecting the grid neutral line directly to the negative pole of the dc bus, the stray capacitance between the PV panels and the ground is bypassed. As a result, the CM ground leakage current can be suppressed completely. Meanwhile, the virtual dc bus is created to provide the negative voltage level for the negative ac grid current generation. Consequently, the required dc bus voltage is still the same as that of the full-bridge inverter. Based on this concept, a novel transformerless inverter topology is derived, in which the virtual dc bus is realized with the switched capacitor technology. It consists of only five power switches, two capacitors, and a single filter inductor. Therefore, the power electronics cost can be curtailed. This advanced topology can be modulated with the unipolar sinusoidal pulse width modulation (SPWM) and the double frequency SPWM to reduce the output current ripple. As a result, a smaller filter inductor can be used to reduce the size and magnetic losses. The advantageous circuit performances of the proposed transformerless topology are analyzed in detail, with the results verified by a 500-W prototype.
Number 696 Computer Laboratory UCAM-CL-TR-696 ISSN 1476-2986 An introduction to inertial navigation

Until recently the weight and size of inertial sensors has prohibited their use in domains such as human motion capture. Recent improvements in the performance of small and lightweight micromachined electromechanical systems (MEMS) inertial sensors have made the application of inertial techniques to such problems possible. This has resulted in an increased interest in the topic of inertial navigation, however current introductions to the subject fail to sufficiently describe the error characteristics of inertial systems. We introduce inertial navigation, focusing on strapdown systems based on MEMS devices. A combination of measurement and simulation is used to explore the error characteristics of such systems. For a simple inertial navigation system (INS) based on the Xsens Mtx inertial measurement unit (IMU), we show that the average error in position grows to over 150 m after 60 seconds of operation. The propagation of orientation errors caused by noise perturbing gyroscope signals is identified as the critical cause of such drift. By simulation we examine the significance of individual noise processes perturbing the gyroscope signals, identifying white noise as the process which contributes most to the overall drift of the system. Sensor fusion and domain specific constraints can be used to reduce drift in INSs. For an example INS we show that sensor fusion using magnetometers can reduce the average error in position obtained by the system after 60 seconds from over 150 m to around 5 m. We conclude that whilst MEMS IMU technology is rapidly improving, it is not yet possible to build a MEMS based INS which gives sub-meter position accuracy for more than one minute of operation.
Semi-Supervised Sequential Labeling and Segmentation Using Giga-Word Scale Unlabeled Data

This paper provides evidence that the use of more unlabeled data in semi-supervised learning can improve the performance of Natural Language Processing (NLP) tasks, such as part-of-speech tagging, syntactic chunking, and named entity recognition. We first propose a simple yet powerful semi-supervised discriminative model appropriate for handling large scale unlabeled data. Then, we describe experiments performed on widely used test collections, namely, PTB III data, CoNLL’00 and ’03 shared task data for the above three NLP tasks, respectively. We incorporate up to 1G-words (one billion tokens) of unlabeled data, which is the largest amount of unlabeled data ever used for these tasks, to investigate the performance improvement. In addition, our results are superior to the best reported results for all of the above test collections.
Invariant Scattering Convolution Networks.

A wavelet scattering network computes a translation invariant image representation, which is stable to deformations and preserves high frequency information for classification. It cascades wavelet transform convolutions with non-linear modulus and averaging operators. The first network layer outputs SIFT-type descriptors whereas the next layers provide complementary invariant information which improves classification. The mathematical analysis of wavelet scattering networks explain important properties of deep convolution networks for classification. A scattering representation of stationary processes incorporates higher order moments and can thus discriminate textures having same Fourier power spectrum. State of the art classification results are obtained for handwritten digits and texture discrimination, with a Gaussian kernel SVM and a generative PCA classifier.
Exploitation and exploration in a performance based contextual advertising system

The dynamic marketplace in online advertising calls for ranking systems that are optimized to consistently promote and capitalize better performing ads. The streaming nature of online data inevitably makes an advertising system choose between maximizing its expected revenue according to its current knowledge in short term (exploitation) and trying to learn more about the unknown to improve its knowledge (exploration), since the latter might increase its revenue in the future. The exploitation and exploration (EE) tradeoff has been extensively studied in the reinforcement learning community, however, not been paid much attention in online advertising until recently. In this paper, we develop two novel EE strategies for online advertising. Specifically, our methods can adaptively balance the two aspects of EE by automatically learning the optimal tradeoff and incorporating confidence metrics of historical performance. Within a deliberately designed offline simulation framework we apply our algorithms to an industry leading performance based contextual advertising system and conduct extensive evaluations with real online event log data. The experimental results and detailed analysis reveal several important findings of EE behaviors in online advertising and demonstrate that our algorithms perform superiorly in terms of ad reach and click-through-rate (CTR).
An RCP Packaged Transceiver Chipset for Automotive LRR and SRR Systems in SiGe BiCMOS Technology

We present a transceiver chipset consisting of a four channel receiver (Rx) and a single-channel transmitter (Tx) designed in a 200-GHz SiGe BiCMOS technology. Each Rx channel has a conversion gain of 19 dB with a typical single sideband noise figure of 10 dB at 1-MHz offset. The Tx includes two exclusively-enabled voltage-controlled oscillators on the same die to switch between two bands at 76-77 and 77-81 GHz. The phase noise is -97 dBc/Hz at 1-MHz offset. On-wafer, the output power is 2 × 13 dBm. At 3.3-V supply, the Rx chip draws 240 mA, while the Tx draws 530 mA. The power dissipation for the complete chipset is 2.5 W. The two chips are used as vehicles for a 77-GHz package test. The chips are packaged using the redistribution chip package technology. We compare on-wafer measurements with on-board results. The loss at the RF port due to the transition in the package results to be less than 1 dB at 77 GHz. The results demonstrate an excellent potential of the presented millimeter-wave package concept for millimeter-wave applications.
Equivalence and Efficiency of Image Alignment Algorithms

There are two major formulations of image alignment using gradient descent. The first estimates an additive increment to the parameters (the additiveapproach), the second an incremental warp (the compositional approach). We first prove that these two formulations are equivalent. A very efficient algorithm was recently proposed by Hager and Belhumeur using the additive approach that unfortunately can only be applied to a very restricted class of warps. We show that using the compositional approach an equally efficient algorithm (theinverse compositional algorithm) can be derived that can be applied to any set of warps which form a group. While most warps used in computer vision form groups, there are a certain warps that do not. Perhaps most notable is the set of piecewise affine warps used in Flexible Appearance Models (FAMs). We end this paper by extending the inverse compositional algorithm to apply to FAMs.
MAC Essentials for Wireless Sensor Networks

The wireless medium being inherently broadcast in nature and hence prone to interferences requires highly optimized medium access control (MAC) protocols. This holds particularly true for wireless sensor networks (WSNs) consisting of a large amount of miniaturized battery-powered wireless networked sensors required to operate for years with no human intervention. There has hence been a growing interest on understanding and optimizing WSN MAC protocols in recent years, where the limited and constrained resources have driven research towards primarily reducing energy consumption of MAC functionalities. In this paper, we provide a comprehensive state-of-the-art study in which we thoroughly expose the prime focus of WSN MAC protocols, design guidelines that inspired these protocols, as well as drawbacks and shortcomings of the existing solutions and how existing and emerging technology will influence future solutions. In contrast to previous surveys that focused on classifying MAC protocols according to the technique being used, we provide a thematic taxonomy in which protocols are classified according to the problems dealt with. We also show that a key element in selecting a suitable solution for a particular situation is mainly driven by the statistical properties of the generated traffic.