nils-herrmann/clean_scirepeval huggingface dataset:
ICDAR2017 Competition on Page Object Detection

This paper presents the results of ICDAR2017 Competition on Page Object Detection (POD). POD is to detect page objects (tables, mathematical equations, graphics, figures, etc.) from document images. This competition makes use of a dataset consists of 2,000 document page images This dataset contains abundant page objects with various types and layouts. During the competition, we received 13 different teams' registrations and finally 8 of them submitted their results. All teams used deep learning as the basic method, then combined different traditional features or methods to improve the detection performance. The team NLPR-PAL achieved the averaged F1 of 0.898 and mAPs of 0.805 in the detection of all page objects under the IOU threshold 0.8. In this overview paper, we summarize the task design, dataset, results, and the approaches used by those teams of this competitions.
New regenerative active snubber circuit for ZVS phase shift Full Bridge converter

The Full Bridge converter topology modulated in phase shift is one of the most popular converters used to obtain high efficiency conversion, especially in high power and high voltage applications. This converter topology combines the simplicity of fixed frequency modulations with the soft switching characteristic of resonant converters but, if a diode rectifier is used as output stage, it suffers of severe overshoot voltage spikes and ringing across the rectifier. In this paper, a new regenerative active snubber is widely studied and developed to reduce this drawback. The proposed snubber is based on a combination of an active clamp with a buck converter used to discharge the snubber capacitor. The snubber gate signal is obtained by using those of the phase shift modulation.
Formalizing information security knowledge

Unified and formal knowledge models of the information security domain are fundamental requirements for supporting and enhancing existing risk management approaches. This paper describes a security ontology which provides an ontological structure for information security domain knowledge. Besides existing best-practice guidelines such as the German IT Grundschutz Manual also concrete knowledge of the considered organization is incorporated. An evaluation conducted by an information security expert team has shown that this knowledge model can be used to support a broad range of information security risk management approaches.
Compact Metamaterial-Based UHF RFID Antennas : Deformed Omega and Split-Ring Resonator Structures

Over the past decade, researchers have shown significant advances in the area of radio frequency identification (RFID) and metamaterials. RFID is being applied to a wide spectrum of industries and metamaterial-based antennas are beginning to perform just as well as existing larger printed antennas. This paper presents two novel metamaterial-based antennas for passive ultra-high frequency (UHF) RFID tags. It is shown that by implementing omega-like elements and split-ring resonators into the design of an antenna for an UHF RFID tag, the overall size of the antenna can be significantly reduced to dimensions of less than 0.15λ0, while preserving the performance of the antenna.
On Deep Learning for Trust-Aware Recommendations in Social Networks

With the emergence of online social networks, the social network-based recommendation approach is popularly used. The major benefit of this approach is the ability of dealing with the problems with cold-start users. In addition to social networks, user trust information also plays an important role to obtain reliable recommendations. Although matrix factorization (MF) becomes dominant in recommender systems, the recommendation largely relies on the initialization of the user and item latent feature vectors. Aiming at addressing these challenges, we develop a novel trust-based approach for recommendation in social networks. In particular, we attempt to leverage deep learning to determinate the initialization in MF for trust-aware social recommendations and to differentiate the community effect in user’s trusted friendships. A two-phase recommendation process is proposed to utilize deep learning in initialization and to synthesize the users’ interests and their trusted friends’ interests together with the impact of community effect for recommendations. We perform extensive experiments on real-world social network data to demonstrate the accuracy and effectiveness of our proposed approach in comparison with other state-of-the-art methods.
PDF Scrutinizer: Detecting JavaScript-based attacks in PDF documents

For a long time PDF documents have arrived in the everyday life of the average computer user, corporate businesses and critical structures, as authorities and military. Due to its wide spread in general, and because out-of-date versions of PDF readers are quite common, using PDF documents has become a popular malware distribution strategy. In this context, malicious documents have useful features: they are trustworthy, attacks can be camouflaged by inconspicuous document content, but still, they can often download and install malware undetected by firewall and anti-virus software. In this paper we present PDF Scrutinizer, a malicious PDF detection and analysis tool. We use static, as well as, dynamic techniques to detect malicious behavior in an emulated environment. We evaluate the quality and the performance of the tool with PDF documents from the wild, and show that PDF Scrutinizer reliably detects current malicious documents, while keeping a low false-positive rate and reasonable runtime performance.
Correlated Failures, Diversification, and Information Security Risk Management

The increasing dependence on information networks for business operations has focused managerial attention on managing risks posed by failure of these networks. In this paper, we develop models to assess the risk of failure on the availability of an information network due to attacks that exploit software vulnerabilities. Software vulnerabilities arise from software installed on the nodes of the network. When the same software stack is installed on multiple nodes on the network, software vulnerabilities are shared among them. These shared vulnerabilities can result in correlated failure of multiple nodes resulting in longer repair times and greater loss of availability of the network. Considering positive network effects (e.g., compatibility) alone without taking the risks of correlated failure and the resulting downtime into account would lead to overinvestment in homogeneous software deployment. Exploiting characteristics unique to information networks, we present a queuing model that allows us to quantify downtime loss faced by a rm as a function of (1) investment in security technologies to avert attacks, (2) software diversification to limit the risk of correlated failure under attacks, and (3) investment in IT resources to repair failures due to attacks. The novelty of this method is that we endogenize the failure distribution and the node correlation distribution, and show how the diversification strategy and other security measures/investments may impact these two distributions, which in turn determine the security loss faced by the firm. We analyze and discuss the effectiveness of diversification strategy under different operating conditions and in the presence of changing vulnerabilities. We also take into account the benefits and costs of a diversification strategy. Our analysis provides conditions under which diversification strategy is advantageous.
Web Content Extraction Through Machine Learning

Web content extraction is a key technology for enabling an array of applications aimed at understanding the web. While automated web extraction has been studied extensively, they often focus on extracting structured data that appear multiple times on a single webpage, like product catalogs. This project aims to extract less structured web content, like news articles, that appear only once in noisy webpages. Our approach classifies text blocks using a mixture of visual and language independent features. In addition, a pipeline is devised to automatically label datapoints through clustering where each cluster is scored based on its relevance to the webpage description extracted from the meta tags, and datapoints in the best cluster are selected as positive training examples.
Modeling and control of a new quadrotor manipulation system

This paper introduces a new quadrotor manipulation system that consists of a 2-link manipulator attached to the bottom of a quadrotor. This new system presents a solution for the drawbacks found in the current quadrotor manipulation system which uses a gripper fixed to a quadrotor. Unlike the current system, the proposed system enables the end-effector to achieve any arbitrary orientation and thus increases its degrees of freedom from 4 to 6. Also, it provides enough distance between the quadrotor and the object to be manipulated. This is useful in some applications such as demining applications. System kinematics and dynamics are derived which are highly nonlinear. Controller is designed based on feedback linearization to track desired trajectories. Controlling the movements in the horizontal directions is simplified by utilizing the derived nonholonmic constraints. Finally, the proposed system is simulated using MATLAB/SIMULINK program. The simulation results show the effectiveness of the proposed controller.
Low cost DC lines PLC based photovoltaic plants parameters smart monitoring communications and control module

Nowadays Photovoltaic (PV) plants have become significant investment projects with long term Return Of Investment (ROI). This is making investors together with operations managers to deal with reliable information on photovoltaic plants performances. Most of the information is gathered through data monitoring systems and also supplied by proper inverters in case of grid connected plants. It usually relates to series/parallel combinations of PV panels strings, in most cases, but rarely to individual PV panels. Furthermore, in case of huge dimensions PV plants, with different ground profiles, etc., should any adverse circumstances happen (panel failure, sudden shadowing, clouds, strong wind), it is difficult to identify the exact problem location. The use of distributed wired or wireless sensors can be a solution. Nevertheless, no one is problems free and all are significant cost. In this article is proposed a low cost DC Power Lines Communications (DC PLC) based PV plant parameters smart monitoring communications and control module. The aim is the development of a micro controller (uC) based sensor module with corresponding modem for communications through already existing DC plant power wiring as data transmission lines. This will reduce drastically both hardware and transmission lines costs.
Oil Rents , Corruption , and State Stability : Evidence From Panel Data Regressions

This Working Paper should not be reported as representing the views of the IMF. The views expressed herein are those of the author and should not be attributed to the IMF, its Executive Board, or its management. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate. We examine the effects of oil rents on corruption and state stability exploiting the exogenous within-country variation of a new measure of oil rents for a panel of 31 oil-exporting countries during the period 1992 to 2005. We find that an increase in oil rents significantly increases corruption, significantly deteriorates political rights while at the same time leading to a significant improvement in civil liberties. We argue that these findings can be explained by the political elite having an incentive to extend civil liberties but reduce political rights in the presence of oil windfalls to evade redistribution and conflict. We support our argument documenting that there is a significant effect of oil rents on corruption in countries with a high share of state participation in oil production while no such link exists in countries where state participation in oil production is low.
Detection of Domain Specific Terminology Using Corpora Comparison

Identifying terms in specialized corpora is a central task in terminological work (compilation of domain-specific dictionaries), but is labour-intensive, especially when the corpora are voluminous which is often the case nowadays. For the past decade, terminologists and specialized lexicographers have been able to rely on term-extraction tools to assist them in the selection of terms. However, most term-extractors focus on the identification of complex terms. Although complex terms (cellular telephone) are central to terminology processing, retrieval of uniterms (telephone) is still a major challenge. This paper evaluates the usefulness of a corpora comparison approach in order to find pinpoint corpus specific words in order to identify uniterms in the field of telecommunications.
Web of Topics: An IoT-aware model-driven designing approach

In the Internet of Things, the extreme heterogeneity of sensors, actuators and user devices calls for new tools and design models able to translate the user's needs in machine-understandable scenarios. The scientific community has proposed different solution for such issue, e.g., the MQTT (MQ Telemetry Transport) protocol introduced the topic concept as “the key that identifies the information channel to which payload data is published”. This study extends the topic approach by proposing the Web of Topics (WoX), a conceptual model for the IoT. A WoX Topic is identified by two coordinates: (i) a discrete semantic feature of interest (e.g. temperature, humidity), and (ii) a URI-based location. An IoT entity defines its role within a Topic by specifying its technological and collaborative dimensions. By this approach, it is easier to define an IoT entity as a set of couples Topic-Role. In order to prove the effectiveness of the WoX approach, we developed the WoX APIs on top of an EPCglobal implementation. Then, 10 developers were asked to build a WoX-based application supporting a physics lab scenario at school. They also filled out an ex-ante and an ex-post questionnaire. A set of qualitative and quantitative metrics allowed measuring the model's outcome.
High-frequency integrated gate drivers for half-bridge GaN power stage

This paper describes three driver options for integrated half-bridge power stage using depletion-mode GaN-on-SiC 0.15μm RF process: an active pull-up driver, a bootstrapped driver, and a modified active pull-up driver. The approaches are evaluated and compared in 5 W, 20 V synchronous Buck converter prototypes operating at 100 MHz switching frequency over a wide range of operating points. Measured efficiency peaks above 91% for the designs using the bootstrap and the modified active pull-up integrated drivers.
A Dataset for Research on Short-Text Conversations

Natural language conversation is widely regarded as a highly difficult problem, which is usually attacked with either rule-based or learning-based models. In this paper we propose a retrieval-based automatic response model for short-text conversation, to exploit the vast amount of short conversation instances available on social media. For this purpose we introduce a dataset of short-text conversation based on the real-world instances from Sina Weibo (a popular Chinese microblog service), which will be soon released to public. This dataset provides rich collection of instances for the research on finding natural and relevant short responses to a given short text, and useful for both training and testing of conversation models. This dataset consists of both naturally formed conversations, manually labeled data, and a large repository of candidate responses. Our preliminary experiments demonstrate that the simple retrieval-based conversation model performs reasonably well when combined with the rich instances in our dataset.
Autonomous reinforcement learning with experience replay.

This paper considers the issues of efficiency and autonomy that are required to make reinforcement learning suitable for real-life control tasks. A real-time reinforcement learning algorithm is presented that repeatedly adjusts the control policy with the use of previously collected samples, and autonomously estimates the appropriate step-sizes for the learning updates. The algorithm is based on the actor-critic with experience replay whose step-sizes are determined on-line by an enhanced fixed point algorithm for on-line neural network training. An experimental study with simulated octopus arm and half-cheetah demonstrates the feasibility of the proposed algorithm to solve difficult learning control problems in an autonomous way within reasonably short time.
Include Me Out: In-Browser Detection of Malicious Third-Party Content Inclusions

Modern websites include various types of third-party content such as JavaScript, images, stylesheets, and Flash objects in order to create interactive user interfaces. In addition to explicit inclusion of third-party content by website publishers, ISPs and browser extensions are hijacking web browsing sessions with increasing frequency to inject third-party content (e.g., ads). However, third-party content can also introduce security risks to users of these websites, unbeknownst to both website operators and users. Because of the often highly dynamic nature of these inclusions as well as the use of advanced cloaking techniques in contemporary malware, it is exceedingly difficult to preemptively recognize and block inclusions of malicious third-party content before it has the chance to attack the user’s system. In this paper, we propose a novel approach to achieving the goal of preemptive blocking of malicious third-party content inclusion through an analysis of inclusion sequences on the Web. We implemented our approach, called Excision, as a set of modifications to the Chromium browser that protects users from malicious inclusions while web pages load. Our analysis suggests that by adopting our in-browser approach, users can avoid a significant portion of malicious third-party content on the Web. Our evaluation shows that Excision effectively identifies malicious content while introducing a low false positive rate. Our experiments also demonstrate that our approach does not negatively impact a user’s browsing experience when browsing popular websites drawn from the Alexa Top 500.
The Interplay of Beauty, Goodness, and Usability in Interactive Products

Two studies considered the interplay between user-perceived usability (i.e., pragmatic attributes), hedonic attributes (e.g., stimulation, identification), goodness (i.e., satisfaction), and beauty of 4 different MP3-player skins. As long as beauty and goodness stress the subjective valuation of a product, both were related to each other. However, the nature of goodness and beauty was found to differ. Goodness depended on both perceived usability and hedonic attributes. Especially after using the skins, perceived usability became a strong determinant of goodness. In contrast, beauty largely depended on identification; a hedonic attribute group, which captures the product’s ability to communicate important personal values to relevant others. Perceived usability as well as goodness was affected by experience (i.e., actual usability, usability problems), whereas hedonic attributes and beauty remained stable over time. All in all, the nature of beauty is rather self-oriented than goal-oriented, whereas goodness relates to both. HUMAN-COMPUTER INTERACTION, 2004, Volume 19, pp. 319–349  Marc Hassenzahl is a psychologist with an interest in applying research on social cognition, judgment, and choice to human–computer interaction; he is a researcher and lecturer in the Institute of Psychology, Social Psychology and Decision-Making, of the Darmstadt University of Technology in Germany.
Spacing Effects in Real-World Classroom Vocabulary Learning

s International, 56, 5204. Pashler, H., Rohrer, D., Cepeda, N. J., & Carpenter, S. K. (2007). Enhancing learning and retarding forgetting: Choices and consequences. Psychonomic Bulletin and Review, 14, 187–1193. Rohrer, D., & Taylor, K. (2006). The effects of overlearning and distributed practice on the retention of mathematics knowledge. Applied Cognitive Psychology, 20, 1209–1224. Seabrook, R., Brown, G. D., & Solity, J. E. (2005). Distributed and massed practice: From laboratory to classroom. Applied Cognitive Psychology, 19, 107–122. Toppino, T. C., & DeMesquita, M. (1984). Effects of spacing repetitions on children’s memory. Journal of Experimental Child Psychology, 37, 637–
Locally Differentially Private Heavy Hitter Identification

The notion of Local Differential Privacy (LDP) enables users to answer sensitive questions while preserving their privacy. The basic LDP frequent oracle protocol enables the aggregator to estimate the frequency of any value. But when the domain of input values is large, finding the most frequent values, also known as the heavy hitters, by estimating the frequencies of all possible values, is computationally infeasible. In this paper, we propose an LDP protocol for identifying heavy hitters. In our proposed protocol, which we call Prefix Extending Method (PEM), users are divided into groups, with each group reporting a prefix of her value. We analyze how to choose optimal parameters for the protocol and identify two design principles for designing LDP protocols with high utility. Experiments on both synthetic and real-world datasets demonstrate the advantage of our proposed protocol.
Driving and Braking Torque Distribution Methods for Front- and Rear-Wheel-Independent Drive-Type Electric Vehicles on Roads With Low Friction Coefficient

This paper focuses on the development of a front- and rear-wheel-independent drive-type electric vehicle (EV) (FRID EV) as a next-generation EV. The ideal characteristics of a FRID EV promote good performance and safety and are the result of structural features that independently control the driving and braking torques of the front and rear wheels. The first characteristic is the failsafe function. This function enables vehicles to continue running without any unexpected or sudden stops, even if one of the propulsion systems fails. The second characteristic is a function that performs efficient acceleration and deceleration on all road surfaces. This function works by distributing the driving or braking torques to the front and rear wheels, taking into consideration load movement. The third characteristic ensures that the vehicle runs safely on roads with a low friction coefficient (μ), such as icy roads. In this paper, we propose a driving torque distribution method when cornering and a braking torque distribution method; these methods are related to the third characteristic, and they are particularly effective when driving on roads with ultralow μ. We verify the effectiveness of the proposed torque control methods through simulations and experiments on the ultralow-μ road surface with a μ of 0.1.
Job Recruitment and Job Seeking Processes: How Technology Can Help

This survey of current job search and recruitment tools focuses on applying a computer-based approach to job matchmaking. The authors present a semantic-based software platform, LO-MATCH--developed in the framework of a European project on lifelong learning--that highlights future research directions. There are three related Web extras that provide supplemental material. The first video [http://youtu.be/yi9gNd_9g8E] introduces job matchmaking and the LO-MATCH platform. The second video [http://youtu.be/OGQ26dM_KjQ] shows how semantic technologies can be used in a job matchmaking scenario. The third video  demonstrates the LO-MATCH job matchmaking platform.