nils-herrmann/clean_scirepeval huggingface dataset:
Overview for the Second Shared Task on Language Identification in Code-Switched Data

We present an overview of the second shared task on language identification in codeswitched data. For the shared task, we had code-switched data from two different language pairs: Modern Standard ArabicDialectal Arabic (MSA-DA) and SpanishEnglish (SPA-ENG). We had a total of nine participating teams, with all teams submitting a system for SPA-ENG and four submitting for MSA-DA. Through evaluation, we found that once again language identification is more difficult for the language pair that is more closely related. We also found that this year’s systems performed better overall than the systems from the previous shared task indicating overall progress in the state of the art for this task.
Legalization of marijuana: potential impact on youth.

As experts in the health care of children and adolescents, pediatricians may be called on to advise legislators concerning the potential impact of changes in the legal status of marijuana on adolescents. Parents, too, may look to pediatricians for advice as they consider whether to support state-level initiatives that propose to legalize the use of marijuana for medical purposes or to decriminalize possession of small amounts of marijuana. This policy statement provides the position of the American Academy of Pediatrics on the issue of marijuana legalization, and the accompanying technical report (available online) reviews what is currently known about the relationship between adolescents' use of marijuana and its legal status to better understand how change might influence the degree of marijuana use by adolescents in the future.
Resource Intensity , Institutions , and Development

— We examine the relationship between resource abundance and several indicators of human welfare. Consistent with the existing literature on the relationship between resource abundance and economic growth we find that, given an initial income level, resource-intensive countries tend to suffer lower levels of human development. While we find only weak support for a direct link between resources and welfare, there is an indirect link that operates through institutional quality. There are also significant differences in the effects that resources have on different measures of institutional quality. These results imply that the ‘‘resource curse’’ is a more encompassing phenomenon than previously considered, and that key differences exist between the effects of different resource types on various aspects of governance and human welfare.
Repeated retrieval during learning is the key to long-term retention q

Tests not only measure the contents of memory, they can also enhance learning and long-term retention. We report two experiments inspired by Tulving’s (1967) pioneering work on the effects of testing on multitrial free recall. Subjects learned lists of words across multiple study and test trials and took a final recall test 1 week after learning. In Experiment 1, repeated testing during learning enhanced retention relative to repeated studying, although alternating study and test trials produced the best retention. In Experiment 2, recalled items were dropped from further studying or further testing to investigate how different types of practice affect retention. Repeated study of previously recalled items did not benefit retention relative to dropping those items from further study. However, repeated recall of previously recalled items enhanced retention by more than 100% relative to dropping those items from further testing. Repeated retrieval of information is the key to long-term retention.
Inductance Calculation of Tooth-Coil Permanent-Magnet Synchronous Machines

Analytical calculation methods for all the major components of the synchronous inductance of tooth-coil permanent-magnet synchronous machines are reevaluated in this paper. The inductance estimation is different in the tooth-coil machine compared with the one in the traditional rotating field winding machine. The accuracy of the analytical torque calculation highly depends on the estimated synchronous inductance. Despite powerful finite element method (FEM) tools, an accurate and fast analytical method is required at an early design stage to find an initial machine design structure with the desired performance. The results of the analytical inductance calculation are verified and assessed in terms of accuracy with the FEM simulation results and with the prototype measurement results.
MAC Protocols for IEEE 802.11ax: Avoiding Collisions on Dense Networks

Wireless networks have become the main form of Internet access. Statistics show that the global mobile Internet penetration should exceed 70% until 2019. Wi-Fi is an important player in this change. Founded on IEEE 802.11, this technology has a crucial impact in how we share broadband access both in domestic and corporate networks. However, recent works have indicated performance issues in Wi-Fi networks, mainly when they have been deployed without planning and under high user density. Hence, different collision avoidance techniques and Medium Access Control protocols have been designed in order to improve Wi-Fi performance. Analyzing the collision problem, this work strengthens the claims found in the literature about the low Wi-Fi performance under dense scenarios. Then, in particular, this article overviews the MAC protocols used in the IEEE 802.11 standard and discusses solutions to mitigate collisions. Finally, it contributes presenting future trends in MAC protocols. This assists in foreseeing expected improvements for the next generation of Wi-Fi devices.
Data Security and Privacy-Preserving in Edge Computing Paradigm: Survey and Open Issues

With the explosive growth of Internet of Things devices and massive data produced at the edge of the network, the traditional centralized cloud computing model has come to a bottleneck due to the bandwidth limitation and resources constraint. Therefore, edge computing, which enables storing and processing data at the edge of the network, has emerged as a promising technology in recent years. However, the unique features of edge computing, such as content perception, real-time computing, and parallel processing, has also introduced several new challenges in the field of data security and privacy-preserving, which are also the key concerns of the other prevailing computing paradigms, such as cloud computing, mobile cloud computing, and fog computing. Despites its importance, there still lacks a survey on the recent research advance of data security and privacy-preserving in the field of edge computing. In this paper, we present a comprehensive analysis of the data security and privacy threats, protection technologies, and countermeasures inherent in edge computing. Specifically, we first make an overview of edge computing, including forming factors, definition, architecture, and several essential applications. Next, a detailed analysis of data security and privacy requirements, challenges, and mechanisms in edge computing are presented. Then, the cryptography-based technologies for solving data security and privacy issues are summarized. The state-of-the-art data security and privacy solutions in edge-related paradigms are also surveyed. Finally, we propose several open research directions of data security in the field of edge computing.
What makes computer games fun? (abstract only)

One can't deny the effectiveness of video arcade games in reachipg users! Just loop at the number of quarters pushed into the slots, the time spent by people of widely differing abilities, and the number of repeat encounters with the systems. At least part of the success is due to the ease of getting started (the first play of the game gets one comfortable with the procedures), the high degree of visualization of controls and results, and the responsiveness overall. Other factors will be taken up by the panelists.Review of the home computer market shows what can be accomplished by an easy-to-use accounting aid through advertising store demonstrations, and word of mouth. Visicalc has sold over a million dollars! Attendees will have an opportunity to try some of these impressive applications before and after the session.
Improving Hate Speech Detection with Deep Learning Ensembles

Hate speech has become a major issue that is currently a hot topic in the domain of social media. Simultaneously, current proposed methods to address the issue raise concerns about censorship. Broadly speaking, our research focus is the area human rights, including the development of new methods to identify and better address discrimination while protecting freedom of expression. As neural network approaches are becoming state of the art for text classification problems, an ensemble method is adapted for usage with neural networks and is presented to better classify hate speech. Our method utilizes a publicly available embedding model, which is tested against a hate speech corpus from Twitter. To confirm robustness of our results, we additionally test against a popular sentiment dataset. Given our goal, we are pleased that our method has a nearly 5 point improvement in F-measure when compared to original work on a publicly available hate speech evaluation dataset. We also note difficulties encountered with reproducibility of deep learning methods and comparison of findings from other work. Based on our experience, more details are needed in published work reliant on deep learning methods, with additional evaluation information a consideration too. This information is provided to foster discussion within the research community for future work.
Microservices Validation: Methodology and Implementation

Due to the wide spread of cloud computing, arises actual question about architecture, design and implementation of cloud applications. The microservice model describes the design and development of loosely coupled cloud applications when computing resources are provided on the basis of automated IaaS and PaaS cloud platforms. Such applications consist of hundreds and thousands of service instances, so automated validation and testing of cloud applications developed on the basis of microservice model is a pressing issue. There are constantly developing new methods of testing both individual microservices and cloud applications at a whole. This article presents our vision of a framework for the validation of the microservice cloud applications, providing an integrated approach for the implementation of various testing methods of such applications, from basic unit tests to continuous stability testing.
Who did What: A Large-Scale Person-Centered Cloze Dataset

We have constructed a new “Who-did-What” dataset of over 200,000 fill-in-the-gap (cloze) multiple choice reading comprehension problems constructed from the LDC English Gigaword newswire corpus. The WDW dataset has a variety of novel features. First, in contrast with the CNN and Daily Mail datasets (Hermann et al., 2015) we avoid using article summaries for question formation. Instead, each problem is formed from two independent articles — an article given as the passage to be read and a separate article on the same events used to form the question. Second, we avoid anonymization — each choice is a person named entity. Third, the problems have been filtered to remove a fraction that are easily solved by simple baselines, while remaining 84% solvable by humans. We report performance benchmarks of standard systems and propose the WDW dataset as a challenge task for the
Fighting unicode-obfuscated spam

In the last few years, obfuscation has been used more and more by spammers to make spam emails bypass filters. The standard method is to use images that look like text, since typical spam filters are unable to parse such messages; this is what is used in so-called "rock phishing". To fight image-based spam, many spam filters use heuristic rules in which emails containing images are flagged, and since not many legit emails are composed mainly of a big image, this aids in detecting image-based spam. The spammers are thus interested in circumventing these methods. Unicode transliteration is a convenient tool for spammers, since it allows a spammer to create a large number of homomorphic clones of the same looking message; since Unicode contains many characters that are unique but appear very similar, spammers can translate a message's characters at random to hide black-listed words in an effort to bypass filters. In order to defend against these unicode-obfuscated spam emails, we developed a prototype tool that can be used with Spam Assassin to block spam obfuscated in this way by mapping polymorphic messages to a common, more homogeneous representation. This representation can then be filtered using traditional methods. We demonstrate the ease with which Unicode polymorphism can be used to circumvent spam filters such as SpamAssassin, and then describe a de-obfuscation technique that can be used to catch messages that have been obfuscated in this fashion.
Diffusion Independent Semi-Bandit Influence Maximization

We consider influence maximization (IM) in social networks, which is the problem of maximizing the number of users that become aware of a product by selecting a set of “seed” users to expose the product to. While prior work assumes a known model of information diffusion, we propose a parametrization in terms of pairwise reachability which makes our framework agnostic to the underlying diffusion model. We give a corresponding monotone, submodular surrogate function, and show that it is a good approximation to the original IM objective. We also consider the case of a new marketer looking to exploit an existing social network, while simultaneously learning the factors governing information propagation. For this, we propose a pairwise-influence semi-bandit feedback model and develop a LinUCB-based bandit algorithm. Our model-independent regret analysis shows that our bound on the cumulative regret has a better (as compared to previous work) dependence on the size of the network. By using the graph Laplacian eigenbasis to construct features, we describe a practical LinUCB implementation. Experimental evaluation suggests that our framework is robust to the underlying diffusion model and can efficiently learn a near-optimal solution.
Negotiating Masculinity in J . D . Salinger ’ s Catcher in the Rye

This analysis focuses on several masculinities conceptualized by Holden Caulfield within the sociohistorical context of the 1950s. When it is understood by heterosexual men that to be masculine means to be emotionally detached and viewing women as sexual objects, they help propagate the belief that expressions of femininity and non-hegemonic masculinities are abnormal behavior and something to be demonized and punished. I propose that Holden’s “craziness” is the result of there being no positive images of heteronormative masculinity and no alternative to the rigid and strictly enforced roles offered to boys as they enter manhood. I suggest that a paradigm shift is needed that starts by collectively recognizing our current forms of patriarchy as harmful to everyone, followed by a reevaluation of how gender is prescribed to youth.
Simplifying the DevOps Adoption Process

DevOps is a new tendency in business and information technology alignment. The purpose of DevOps is bridging the gap between the development and operations. Several sources claim that DevOps is a new style of work. Many successful DevOps introduction attempts and also many problems in adoption of this style of work have been discussed. This paper reports on research results in facilitating the adoption of DevOps in small enterprises. The DevOps adoption method and several related to it artefacts are proposed. The proposed method has been tested in a national branch of an international company with an internal IT development team.
Securing the Internet of Things: New Perspectives and Research Challenges

The Internet of Things (IoT) realizes a vision where billions of interconnected devices are deployed just about everywhere, from inside our bodies to the most remote areas of the globe. As the IoT will soon pervade every aspect of our lives and will be accessible from anywhere, addressing critical IoT security threats is now more important than ever. Traditional approaches where security is applied as an afterthought and as a “patch” against known attacks are insufficient. IoT challenges require a new secure-by-design vision, where threats are addressed proactively and IoT devices learn to dynamically adapt to different threats. In this paper, we first provide a taxonomy and survey the state of the art in IoT security research, and offer a roadmap of concrete research challenges to address existing and next-generation IoT security threats.
Commentary : Setting a Definition , Context , and Theory-Based Research Agenda for the Gamification of Non-Gaming Applications

As a nascent area of study, gamification has attracted the interest of researchers in several fields, but such researchers have scarcely focused on creating a theoretical foundation for gamification research. Gamification involves using gamelike features in non-game contexts to motivate users and improve performance outcomes. As a boundary-spanning subject by nature, gamification has drawn the interest of scholars from diverse communities, such as information systems, education, marketing, computer science, and business administration. To establish a theoretical foundation, we need to clearly define and explain gamification in comparison with similar concepts and areas of research. Likewise, we need to define the scope of the domain and develop a research agenda that explicitly considers theory’s important role. In this review paper, we set forth the pre-theoretical structures necessary for theory building in this area. Accordingly, we engaged an interdisciplinary group of discussants to evaluate and select the most relevant theories for gamification. Moreover, we developed exemplary research questions to help create a research agenda for gamification. We conclude that using a multi-theoretical perspective in creating a research agenda should help and encourage IS researchers to take a lead role in this promising and emerging area.
Traffic-Sign Detection and Classification in the Wild

Although promising results have been achieved in the areas of traffic-sign detection and classification, few works have provided simultaneous solutions to these two tasks for realistic real world images. We make two contributions to this problem. Firstly, we have created a large traffic-sign benchmark from 100000 Tencent Street View panoramas, going beyond previous benchmarks. It provides 100000 images containing 30000 traffic-sign instances. These images cover large variations in illuminance and weather conditions. Each traffic-sign in the benchmark is annotated with a class label, its bounding box and pixel mask. We call this benchmark Tsinghua-Tencent 100K. Secondly, we demonstrate how a robust end-to-end convolutional neural network (CNN) can simultaneously detect and classify trafficsigns. Most previous CNN image processing solutions target objects that occupy a large proportion of an image, and such networks do not work well for target objects occupying only a small fraction of an image like the traffic-signs here. Experimental results show the robustness of our network and its superiority to alternatives. The benchmark, source code and the CNN model introduced in this paper is publicly available1.
Impact Analysis of NBTI / PBTI on SRAM V MIN and Design Techniques for Improved SRAM V MIN

Negative bias temperature instability (NBTI) and positive bias temperature instability (PBTI) are critical circuit reliability issues in highly scaled CMOS technologies. In this paper, we analyze the impacts of NBTI and PBTI on SRAM VMIN, and present a design solution for mitigating the impact of NBTI and PBTI on SRAM VMIN. Two different types of SRAM VMIN (SNM-limited VMIN and time-limited VMIN) are explained. Simulation results show that SNM-limited VMIN is more sensitive to NBTI while time-limited VMIN is more prone to suffer from PBTI effect. The proposed NBTI/PBTI-aware control of wordline pulse width and woldline voltage improves cell stability, and mitigates the VMIN degradation induced by NBTI/PBTI.
Performance and Evaluation Sobel Edge Detection on Various Methodologies

This paper compares various methodologies for the design of Sobel Edge Detection Algorithm on Field Programmable Gate Arrays (FPGAs). We show some characteristics to design a computer vision algorithm to suitable hardware platforms. We evaluate hardware resources and power consumption of Sobel Edge Detection on two studies: Xilinx system generator (XSG) and Vivado_HLS tools which both are very useful tools for developing computer vision algorithms. The comparison the hardware resources and power consumption among FPGA platforms (Zynq-7000 AP SoC, Spartan 3A DSP) are analyzed. The hardware resources by using Vivado_HLS on both platforms are used less 9 times with BRAM_18K, 7 times with DSP48E, 2 times with FFs, and approximately with LUTs comparing with XSG. In addition, the power consumption on Zynq-7000 AP SoC spends more 30% by using Vivado_HLS than by using XSG tool and for Spartan 3A DSP consumes a half of power comparing with by using XSG tool. In the study by using Vivado_HLS shows that power consumption depends on frequency.
SymNMF: nonnegative low-rank approximation of a similarity matrix for graph clustering

Nonnegative matrix factorization (NMF) provides a lower rank approximation of a matrix by a product of two nonnegative factors. NMF has been shown to produce clustering results that are often superior to those by other methods such as K-means. In this paper, we provide further interpretation of NMF as a clustering method and study an extended formulation for graph clustering called Symmetric NMF (SymNMF). In contrast to NMF that takes a data matrix as an input, SymNMF takes a nonnegative similarity matrix as an input, and a symmetric nonnegative lower rank approximation is computed. We show that SymNMF is related to spectral clustering, justify SymNMF as a general graph clustering method, and discuss the strengths and shortcomings of SymNMF and spectral clustering. We propose two optimization algorithms for SymNMF and discuss their convergence properties and computational efficiencies. Our experiments on document clustering, image clustering, and image segmentation support SymNMF as a graph clustering method that captures latent linear and nonlinear relationships in the data.
Deep Semantic Role Labeling: What Works and What's Next

We introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations. We use a deep highway BiLSTM architecture with constrained decoding, while observing a number of recent best practices for initialization and regularization. Our 8-layer ensemble model achieves 83.2 F1 on the CoNLL 2005 test set and 83.4 F1 on CoNLL 2012, roughly a 10% relative error reduction over the previous state of the art. Extensive empirical analysis of these gains show that (1) deep models excel at recovering long-distance dependencies but can still make surprisingly obvious errors, and (2) that there is still room for syntactic parsers to improve these results.