nils-herrmann/clean_scirepeval huggingface dataset:
Extraction of temporal facts and events from Wikipedia

Recently, large-scale knowledge bases have been constructed by automatically extracting relational facts from text. Unfortunately, most of the current knowledge bases focus on static facts and ignore the temporal dimension. However, the vast majority of facts are evolving with time or are valid only during a particular time period. Thus, time is a significant dimension that should be included in knowledge bases.
 In this paper, we introduce a complete information extraction framework that harvests temporal facts and events from semi-structured data and free text of Wikipedia articles to create a temporal ontology. First, we extend a temporal data representation model by making it aware of events. Second, we develop an information extraction method which harvests temporal facts and events from Wikipedia infoboxes, categories, lists, and article titles in order to build a temporal knowledge base. Third, we show how the system can use its extracted knowledge for further growing the knowledge base.
 We demonstrate the effectiveness of our proposed methods through several experiments. We extracted more than one million temporal facts with precision over 90% for extraction from semi-structured data and almost 70% for extraction from text.
Synthesizing Union Tables from the Web

Benton County, AR Public Schools | PublicSchoolReview Berkshire County, MA Public Schools | PublicSchoolReview Minnesota Special Education Schools | PublicSchoolReview Town School # Students Grades Hidden Attribute 1 (type: location) Hidden Attribute 2 (type: school_category) Bentonville Bentonville High 3333 9-12 Benton County, AR Public Schools Decatur Decatur High School 120 9-12 Benton County, AR Public Schools Adams Berkshire Arts And 216 6-12 Berkshire County, MA Public Schools Cheshire Hoosac Valley High 692 7-12 Berkshire County, MA Public Schools Dalton Wahconah Regional 628 9-12 Berkshire County, MA Public Schools Alexandria Northside Adolescent 6 7-12 Minnesota Special Education Schools Anoka Transition Plus High 206 12 Minnesota Special Education Schools ... ... ... ... ... ... Context WebTables Table S0tching Synthesis Table
Sealed-Glass Proofs: Using Transparent Enclaves to Prove and Sell Knowledge

Trusted hardware systems, such as Intel's new SGX instruction set architecture extension, aim to provide strong confidentiality and integrity assurances for applications. Recent work, however, raises serious concerns about the vulnerability of such systems to side-channel attacks. We propose, formalize, and explore a cryptographic primitive called a Sealed-Glass Proof (SGP) that models computation possible in an isolated execution environment with unbounded leakage, and thus in the face of arbitrary side-channels. A SGP specifically models the capabilities of trusted hardware that can attest to correct execution of a piece of code, but whose execution is transparent, meaning that an application's secrets and state are visible to other processes on the same host. Despite this strong threat model, we show that SGPs enable a range of practical applications. Our key observation is that SGPs permit safe verifiable computing in zero-knowledge, as data leakage results only in the prover learning her own secrets. Among other applications, we describe the implementation of an end-to-end bug bounty (or zero-day solicitation) platform that couples a SGX-based SGP with a smart contract. Our platform enables a marketplace that achieves fair exchange, protects against unfair bounty withdrawals, and resists denial-of-service attacks by dishonest sellers. We also consider a slight relaxation of the SGP model that permits black-box modules instantiating minimal, side-channel resistant primitives, yielding a still broader range of applications. Our work shows how trusted hardware systems such as SGX can support trustworthy applications even in the presence of side channels.
Rendezvous: A search engine for binary code

The problem of matching between binaries is important for software copyright enforcement as well as for identifying disclosed vulnerabilities in software. We present a search engine prototype called Rendezvous which enables indexing and searching for code in binary form. Rendezvous identifies binary code using a statistical model comprising instruction mnemonics, control flow sub-graphs and data constants which are simple to extract from a disassembly, yet normalising with respect to different compilers and optimisations. Experiments show that Rendezvous achieves F2 measures of 86.7% and 83.0% on the GNU C library compiled with different compiler optimisations and the GNU coreutils suite compiled with gcc and clang respectively. These two code bases together comprise more than one million lines of code. Rendezvous will bring significant changes to the way patch management and copyright enforcement is currently performed.
MedLDA: maximum margin supervised topic models for regression and classification

Supervised topic models utilize document's side information for discovering predictive low dimensional representations of documents; and existing models apply likelihood-based estimation. In this paper, we present a max-margin supervised topic model for both continuous and categorical response variables. Our approach, the maximum entropy discrimination latent Dirichlet allocation (MedLDA), utilizes the max-margin principle to train supervised topic models and estimate predictive topic representations that are arguably more suitable for prediction. We develop efficient variational methods for posterior inference and demonstrate qualitatively and quantitatively the advantages of MedLDA over likelihood-based topic models on movie review and 20 Newsgroups data sets.
ROBOCRANE AND EMMA APPLIED TO WASTE STORAGE TANK REMEDIATION

The RoboCrane is a revolutionary concept in crane automation. The RoboCrane, based on the Stewart platform parallel link manipulator design, consists of cables as the parallel links and winches as the actuators. The result is a work platform that can maneuver tools and/or manipulators with full six degree-of-freedom control. EMMA, a serpentine manipulator, is currently being developed for several applications, including the delivery and orientation of end-effectors throughout nuclear waste storage tanks. The combination of RoboCrane and EMMA can provide an adaptable and highly effective means for waste storage tank remediation.
Applications of the conjugate gradient method for implicit feedback collaborative filtering

The need for solving weighted ridge regression (WRR) problems arises in a number of collaborative filtering (CF) algorithms. Often, there is not enough time to calculate the exact solution of the WRR problem, or it is not required. The conjugate gradient (CG) method is a state-of-the-art approach for the approximate solution of WRR problems. In this paper, we investigate some applications of the CG method for new and existing implicit feedback CF models. We demonstrate through experiments on the Netflix dataset that CG can be an efficient tool for training implicit feedback CF models.
Privacy-preserving verifiable data aggregation and analysis for cloud-assisted mobile crowdsourcing

Crowdsourcing is a crowd-based outsourcing, where a requester (task owner) can outsource tasks to workers (public crowd). Recently, mobile crowdsourcing, which can leverage workers' data from smartphones for data aggregation and analysis, has attracted much attention. However, when the data volume is getting large, it becomes a difficult problem for a requester to aggregate and analyze the incoming data, especially when the requester is an ordinary smartphone user or a start-up company with limited storage and computation resources. Besides, workers are concerned about their identity and data privacy. To tackle these issues, we introduce a three-party architecture for mobile crowdsourcing, where the cloud is implemented between workers and requesters to ease the storage and computation burden of the resource-limited requester. Identity privacy and data privacy are also achieved. With our scheme, a requester is able to verify the correctness of computation results from the cloud. We also provide several aggregated statistics in our work, together with efficient data update methods. Extensive simulation shows both the feasibility and efficiency of our proposed solution.
An 8$\,\times\,$ 8 Butler Matrix in 0.13-$\mu{\hbox {m}}$ CMOS for 5–6-GHz Multibeam Applications

This paper presents a miniature 5-6-GHz 8 × 8 Butler matrix in a 0.13-μm CMOS implementation. The 8 × 8 design results in an insertion loss of 3.5 dB at 5.5 GHz with a bandwidth of 5-6 GHz and no power consumption. The chip area is 2.5 × 1.9 mm2 including all pads. The 8 × 8 matrix is mounted on a Teflon board with eight antennas, and the measured patterns agree well with theory and show an isolation of >; 12 dB at 5-6 GHz. CMOS Butler matrices offer a simple and low-power alternative to replace eight-element phased-array systems for high gain transceivers. The applications areas are in high data-rate communications at 5-6 and at 57-66 GHz. They can also be excellent candidates for multiple-input-multiple-output systems.
Boosting Vocabulary Learning by Verbal Cueing During Sleep.

Reactivating memories during sleep by re-exposure to associated memory cues (e.g., odors or sounds) improves memory consolidation. Here, we tested for the first time whether verbal cueing during sleep can improve vocabulary learning. We cued prior learned Dutch words either during non-rapid eye movement sleep (NonREM) or during active or passive waking. Re-exposure to Dutch words during sleep improved later memory for the German translation of the cued words when compared with uncued words. Recall of uncued words was similar to an additional group receiving no verbal cues during sleep. Furthermore, verbal cueing failed to improve memory during active and passive waking. High-density electroencephalographic recordings revealed that successful verbal cueing during NonREM sleep is associated with a pronounced frontal negativity in event-related potentials, a higher frequency of frontal slow waves as well as a cueing-related increase in right frontal and left parietal oscillatory theta power. Our results indicate that verbal cues presented during NonREM sleep reactivate associated memories, and facilitate later recall of foreign vocabulary without impairing ongoing consolidation processes. Likewise, our oscillatory analysis suggests that both sleep-specific slow waves as well as theta oscillations (typically associated with successful memory encoding during wakefulness) might be involved in strengthening memories by cueing during sleep.
Spring Embedders and Force Directed Graph Drawing Algorithms

Force-directed algorithms are among the most flexible methods for calculating layouts of simple undirected graphs. Also known as spring embedders, such algorithms calculate the layout of a graph using only information contained within the structure of the graph itself, rather than relying on domain-specific knowledge. Graphs drawn with these algorithms tend to be aesthetically pleasing, exhibit symmetries, and tend to produce crossing-free layouts for planar graphs. In this survey we consider several classical algorithms, starting from Tutte’s 1963 barycentric method, and including recent scalable multiscale methods for large and dynamic graphs.
A Style-Aware Content Loss for Real-Time HD Style Transfer

Recently, style transfer has received a lot of attention. While much of this research has aimed at speeding up processing, the approaches are still lacking from a principled, art historical standpoint: a style is more than just a single image or an artist, but previous work is limited to only a single instance of a style or shows no benefit from more images. Moreover, previous work has relied on a direct comparison of art in the domain of RGB images or on CNNs pre-trained on ImageNet, which requires millions of labeled object bounding boxes and can introduce an extra bias, since it has been assembled without artistic consideration. To circumvent these issues, we propose a style-aware content loss, which is trained jointly with a deep encoder-decoder network for real-time, high-resolution stylization of images and videos. We propose a quantitative measure for evaluating the quality of a stylized image and also have art historians rank patches from our approach against those from previous work. These and our qualitative results ranging from small image patches to megapixel stylistic images and videos show that our approach better captures the subtle nature in which a style affects content.
On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimization

Some recent works in machine learning and computer vision involve the solution of a bilevel optimization problem. Here the solution of a parameterized lower-level problem binds variables that appear in the objective of an upper-level problem. The lower-level problem typically appears as an argmin or argmax optimization problem. Many techniques have been proposed to solve bi-level optimization problems, including gradient descent, which is popular with current end-to-end learning approaches. In this technical report we collect some results on differentiating argmin and argmax optimization problems with and without constraints and provide some insightful motivating examples.
DCTCP: Efficient Packet Transport for the Commoditized Data Center

Cloud data centers host diverse applications, mixing in the same network a plethora of workflows that require small predictable latency with others requiring large sustained throughput. In this environment, today’s state-of-the-art TCP protocol falls short. We present measurements of a 6000 server production cluster and reveal network impairments, such as queue buildup, buffer pressure, and incast, that lead to high application latencies. Using these insights, propose a variant of TCP, DCTCP, for data center networks. DCTCP leverages Explicit Congestion Notification (ECN) and a simple multibit feedback mechanism at the host. We evaluate DCTCP at 1 and 10Gbps speeds, through benchmark experiments and analysis. In the data center, operating with commodity, shallow buffered switches, DCTCP delivers the same or better throughput than TCP, while using 90% less buffer space. Unlike TCP, it also provides hight burst tolerance and low latency for short flows. While TCP’s limitations cause our developers to restrict the traffic they send today, using DCTCP enables the applications to handle 10X the current background traffic, without impacting foreground traffic. Further, a 10X increase in foreground traffic does not cause any timeouts, thus largely eliminating incast problems.
UWB Antenna With Single or Dual Band-Notches for Lower WLAN Band and Upper WLAN Band

To prevent interference problems due to existing nearby communication systems within an ultrawideband (UWB) operating frequency, the significance of an efficient band-notched design is increased. Here, the band-notches are realized by adding independent controllable strips in terms of the notch frequency and the width of the band-notches to the fork shape of the UWB antenna. The size of the flat type band-notched UWB antenna is etched on 24 times 36 mm2 substrate. Two novel antennas are presented. One antenna is designed for single band-notch with a separated strip to cover the 5.15-5.825 GHz band. The second antenna is designed for dual band-notches using two separated strips to cover the 5.15-5.35 GHz band and 5.725-5.825 GHz band. The simulation and measurement show that the proposed antenna achieves a wide bandwidth from 3 to 12 GHz with the dual band-notches successfully.
Avoidance of Information Technology Threats: A Theoretical Perspective

This paper describes the development of the technology threat avoidance theory (TTAT), which explains individual IT users’ behavior of avoiding the threat of malicious information technologies. We articulate that avoidance and adoption are two qualitatively different phenomena and contend that technology acceptance theories provide a valuable, but incomplete, understanding of users’ IT threat avoidance behavior. Drawing from cybernetic theory and coping theory, TTAT delineates the avoidance behavior as a dynamic positive feedback loop in which users go through two cognitive processes, threat appraisal and coping appraisal, to decide how to cope with IT threats. In the threat appraisal, users will perceive an IT threat if they believe that they are susceptible Alan Dennis was the accepting senior editor for this paper. to malicious IT and that the negative consequences are severe. The threat perception leads to coping appraisal, in which users assess the degree to which the IT threat can be avoided by taking safeguarding measures based on perceived effectiveness and costs of the safeguarding measure and selfefficacy of taking the safeguarding measure. TTAT posits that users are motivated to avoid malicious IT when they perceive a threat and believe that the threat is avoidable by taking safeguarding measures; if users believe that the threat cannot be fully avoided by taking safeguarding measures, they would engage in emotion-focused coping. Integrating process theory and variance theory, TTAT enhances our understanding of human behavior under IT threats and makes an important contribution to IT security research and practice.
Human Activity Recognition Based on Spatial Distribution of Gradients at Sublevels of Average Energy Silhouette Images

The aim of this paper is to present a unified framework for human action and activity recognition by analysing the effect of computation of spatial distribution of gradients (SDGs) on average energy silhouette images (AESIs). Based on the analysis of SDGs computation at various decomposition levels, an effective approach to compute the SDGs is developed. The AESI is constructed for the representation of the shape of action and activity and these are the reflection of 3-D pose into 2-D pose. To describe the AESIs, the SDGs at various sublevels and sum of the directional pixels (SDPs) variations is computed. The temporal content of the activity is computed through R-transform (RT). Finally, the shape computed through SDGs and SDPs, and temporal evidences through RT of the human body is fused together at the recognition stage, which results in a new powerful unified feature map model. The performance of the proposed framework is evaluated on three different publicly available datasets, i.e., Weizmann, KTH, and Ballet and the recognition accuracy is computed using hybrid classifier. The highest recognition accuracy achieved on these datasets is compared with the similar state-of-the-art techniques and demonstrate the superior performance.
Coherent optical OFDM: theory and design.

Coherent optical OFDM (CO-OFDM) has recently been proposed and the proof-of-concept transmission experiments have shown its extreme robustness against chromatic dispersion and polarization mode dispersion. In this paper, we first review the theoretical fundamentals for CO-OFDM and its channel model in a 2x2 MIMO-OFDM representation. We then present various design choices for CO-OFDM systems and perform the nonlinearity analysis for RF-to-optical up-converter. We also show the receiver-based digital signal processing to mitigate self-phase-modulation (SPM) and Gordon-Mollenauer phase noise, which is equivalent to the midspan phase conjugation.
Asymptotic Convergence Rate of the EM Algorithm for Gaussian Mixtures

It is well known that the convergence rate of the expectation-maximization (EM) algorithm can be faster than those of convention first-order iterative algorithms when the overlap in the given mixture is small. But this argument has not been mathematically proved yet. This article studies this problem asymptotically in the setting of gaussian mixtures under the theoretical framework of Xu and Jordan (1996). It has been proved that the asymptotic convergence rate of the EM algorithm for gaussian mixtures locally around the true solution is o(e0.5()), where > 0 is an arbitrarily small number, o(x) means that it is a higher-order infinitesimal as x 0, and e() is a measure of the average overlap of gaussians in the mixture. In other words, the large sample local convergence rate for the EM algorithm tends to be asymptotically superlinear when e() tends to zero.
FairPlay: Fraud and Malware Detection in Google Play

Fraudulent behaviors in Google’s Android app market fuel search rank abuse and malware proliferation. We present FairPlay, a novel system that uncovers both malware and search rank fraud apps, by picking out trails that fraudsters leave behind. To identify suspicious apps, FairPlay’s PCF algorithm correlates review activities and uniquely combines detected review relations with linguistic and behavioral signals gleaned from longitudinal Google Play app data. We contribute a new longitudinal app dataset to the community, which consists of over 87K apps, 2.9M reviews, and 2.4M reviewers, collected over half a year. FairPlay achieves over 95% accuracy in classifying gold standard datasets of malware, fraudulent and legitimate apps. We show that 75% of the identified malware apps engage in search rank fraud. FairPlay discovers hundreds of fraudulent apps that currently evade Google Bouncer’s detection technology, and reveals a new type of attack campaign, where users are harassed into writing positive reviews, and install and review other apps.
An Evaluation of the Google Chrome Extension Security Architecture

Vulnerabilities in browser extensions put users at risk by providing a way for website and network attackers to gain access to users’ private data and credentials. Extensions can also introduce vulnerabilities into the websites that they modify. In 2009, Google Chrome introduced a new extension platform with several features intended to prevent and mitigate extension vulnerabilities: strong isolation between websites and extensions, privilege separation within an extension, and an extension permission system. We performed a security review of 100 Chrome extensions and found 70 vulnerabilities across 40 extensions. Given these vulnerabilities, we evaluate how well each of the security mechanisms defends against extension vulnerabilities. We find that the mechanisms mostly succeed at preventing direct web attacks on extensions, but new security mechanisms are needed to protect users from network attacks on extensions, website metadata attacks on extensions, and vulnerabilities that extensions add to websites. We propose and evaluate additional defenses, and we conclude that banning HTTP scripts and inline scripts would prevent 47 of the 50 most severe vulnerabilities with only modest impact on developers.
The importance of Being Recurrent for Modeling Hierarchical Structure

Recent work has shown that recurrent neural networks (RNNs) can implicitly capture and exploit hierarchical information when trained to solve common natural language processing tasks (Blevins et al., 2018) such as language modeling (Linzen et al., 2016; Gulordava et al., 2018) and neural machine translation (Shi et al., 2016). In contrast, the ability to model structured data with non-recurrent neural networks has received little attention despite their success in many NLP tasks (Gehring et al., 2017; Vaswani et al., 2017). In this work, we compare the two architectures—recurrent versus non-recurrent—with respect to their ability to model hierarchical structure and find that recurrency is indeed important for this purpose. The code and data used in our experiments is available at https://github.com/