nils-herrmann/clean_scirepeval huggingface dataset:
Strike a balance : Users ' expertise on interface

Computers and users process information in distinct ways -so do individual users. Although it's relatively easy to get a computer to understand input, what with fixed standards and universal APIs, usability with human users is not absolute. User-interface usability is relative to the experience level of individual users. UI designer Mike Padilla provides an overview of UI design for Web-based productivity software with a focus on the broadest range of users, examining what makes an application UI-usable and detailing concepts that can facilitate an efficient, broad-based UI design.
Tapas: design, implementation, and usability evaluation of a password manager

Passwords continue to prevail on the web as the primary method for user authentication despite their well-known security and usability drawbacks. Password managers offer some improvement without requiring server-side changes. In this paper, we evaluate the security of dual-possession authentication, an authentication approach offering encrypted storage of passwords and theft-resistance without the use of a master password. We further introduce Tapas, a concrete implementation of dual-possession authentication leveraging a desktop computer and a smartphone. Tapas requires no server-side changes to websites, no master password, and protects all the stored passwords in the event either the primary or secondary device (e.g., computer or phone) is stolen. To evaluate the viability of Tapas as an alternative to traditional password managers, we perform a 30 participant user study comparing Tapas to two configurations of Firefox's built-in password manager. We found users significantly preferred Tapas. We then improve Tapas by incorporating feedback from this study, and reevaluate it with an additional 10 participants.
Beyond word embeddings: learning entity and concept representations from large scale knowledge bases

Text representations using neural word embeddings have proven effective in many NLP applications. Recent researches adapt the traditional word embedding models to learn vectors of multiword expressions (concepts/entities). However, these methods are limited to textual knowledge bases (e.g., Wikipedia). In this paper, we propose a novel and simple technique for integrating the knowledge about concepts from two large scale knowledge bases of different structure (Wikipedia and Probase) in order to learn concept representations. We adapt the efficient skip-gram model to seamlessly learn from the knowledge in Wikipedia text and Probase concept graph. We evaluate our concept embedding models on two tasks: (1) analogical reasoning, where we achieve a state-of-the-art performance of 91% on semantic analogies, (2) concept categorization, where we achieve a state-of-the-art performance on two benchmark datasets achieving categorization accuracy of 100% on one and 98% on the other. Additionally, we present a case study to evaluate our model on unsupervised argument type identification for neural semantic parsing. We demonstrate the competitive accuracy of our unsupervised method and its ability to better generalize to out of vocabulary entity mentions compared to the tedious and error prone methods which depend on gazetteers and regular expressions.
Domain Adaptation in Computer Vision Applications

The aim of this chapter is to give an overview of domain adaptation and transfer learning with a specific view to visual applications. After a general motivation, we first position domain adaptation in the more general transfer learning problem. Second, we try to address and analyze briefly the state-of-the-art methods for different types of scenarios, first describing the historical shallow methods, addressing both the homogeneous and heterogeneous domain adaptation methods. Third, we discuss the effect of the success of deep convolutional architectures which led to the new type of domain adaptation methods that integrate the adaptation within the deep architecture. Fourth, we review DA methods that go beyond image categorization, such as object detection, image segmentation, video analyses or learning visual attributes. We conclude the chapter with a section where we relate domain adaptation to other machine learning solutions.
RecResNet: A Recurrent Residual CNN Architecture for Disparity Map Enhancement

We present a neural network architecture applied to the problem of refining a dense disparity map generated by a stereo algorithm to which we have no access. Our approach is able to learn which disparity values should be modified and how, from a training set of images, estimated disparity maps and the corresponding ground truth. Its only input at test time is a disparity map and the reference image. Two design characteristics are critical for the success of our network: (i) it is formulated as a recurrent neural network, and (ii) it estimates the output refined disparity map as a combination of residuals computed at multiple scales, that is at different up-sampling and down-sampling rates. The first property allows the network, which we named RecResNet, to progressively improve the disparity map, while the second property allows the corrections to come from different scales of analysis, addressing different types of errors in the current disparity map. We present competitive quantitative and qualitative results on the KITTI 2012 and 2015 benchmarks that surpass the accuracy of previous disparity refinement methods.
Blockchains and Smart Contracts for the Internet of Things

Motivated by the recent explosion of interest around blockchains, we examine whether they make a good fit for the Internet of Things (IoT) sector. Blockchains allow us to have a distributed peer-to-peer network where non-trusting members can interact with each other without a trusted intermediary, in a verifiable manner. We review how this mechanism works and also look into smart contracts-scripts that reside on the blockchain that allow for the automation of multi-step processes. We then move into the IoT domain, and describe how a blockchain-IoT combination: 1) facilitates the sharing of services and resources leading to the creation of a marketplace of services between devices and 2) allows us to automate in a cryptographically verifiable manner several existing, time-consuming workflows. We also point out certain issues that should be considered before the deployment of a blockchain network in an IoT setting: from transactional privacy to the expected value of the digitized assets traded on the network. Wherever applicable, we identify solutions and workarounds. Our conclusion is that the blockchain-IoT combination is powerful and can cause significant transformations across several industries, paving the way for new business models and novel, distributed applications.
Fast Parametric Learning with Activation Memorization

For WikiText-103 we swept over LSTM hidden sizes {1024, 2048, 4096}, no. LSTM layers {1, 2}, embedding dropout {0, 0.1, 0.2, 0.3}, use of layer norm (Ba et al., 2016b) , and whether to share the input/output embedding parameters {True,False} totalling 96 parameters. A single-layer LSTM with 2048 hidden units with tied embedding parameters and an input dropout rate of 0.3 was selected, and we used this same model configuration for the other language corpora. We trained the models on 8 P100 Nvidia GPUs by splitting the batch size into 8 sub-batches, sending them to each GPU and summing the resulting gradients. The total batch size used was 512 and a sequence length of 100 was chosen. Gradients were clipped to a maximum norm value of 0.1. We did not pass the state of the LSTM between sequences during training, however the state is passed during evaluation.
The great struggles of life: Darwin and the emergence of evolutionary psychology.

Darwin envisioned a scientific revolution for psychology. His theories of natural and sexual selection identified two classes of struggles--the struggle for existence and the struggle for mates. The emergence of evolutionary psychology and related disciplines signals the fulfillment of Darwin's vision. Natural selection theory guides scientists to discover adaptations for survival. Sexual selection theory illuminates the sexual struggle, highlighting mate choice and same-sex competition adaptations. Theoretical developments since publication of On the Origin of Species identify important struggles unknown to Darwin, notably, within-families conflicts and conflict between the sexes. Evolutionary psychology synthesizes modern evolutionary biology and psychology to penetrate some of life's deep mysteries: Why do many struggles center around sex? Why is social conflict pervasive? And what are the mechanisms of mind that define human nature?
Framework for developing volcanic fragility and vulnerability functions for critical infrastructure

Volcanic risk assessment using probabilistic models is increasingly desired for risk management, particularly for loss forecasting, critical infrastructure management, land-use planning and evacuation planning. Over the past decades this has motivated the development of comprehensive probabilistic hazard models. However, volcanic vulnerability models of equivalent sophistication have lagged behind hazard modelling because of the lack of evidence, data and, until recently, minimal demand. There is an increasingly urgent need for development of quantitative volcanic vulnerability models, including vulnerability and fragility functions, which provide robust quantitative relationships between volcanic impact (damage and disruption) and hazard intensity. The functions available to date predominantly quantify tephra fall impacts to buildings, driven by life safety concerns. We present a framework for establishing quantitative relationships between volcanic impact and hazard intensity, specifically through the derivation of vulnerability and fragility functions. We use tephra thickness and impacts to key infrastructure sectors as examples to demonstrate our framework. Our framework incorporates impact data sources, different impact intensity scales, preparation and fitting of data, uncertainty analysis and documentation. The primary data sources are post-eruption impact assessments, supplemented by laboratory experiments and expert judgment, with the latter drawing upon a wealth of semi-quantitative and qualitative studies. Different data processing and function fitting techniques can be used to derive functions; however, due to the small datasets currently available, simplified approaches are discussed. We stress that documentation of data processing, assumptions and limitations is the most important aspect of function derivation; documentation provides transparency and allows others to update functions more easily. Following our standardised approach, a volcanic risk scientist can derive a fragility or vulnerability function, which then can be easily compared to existing functions and updated as new data become available. To demonstrate how to apply our framework, we derive fragility and vulnerability functions for discrete tephra fall impacts to electricity supply, water supply, wastewater and transport networks. These functions present the probability of an infrastructure site or network component equalling or exceeding one of four impact states as a function of tephra thickness.
A Decade of Lattice Cryptography

Lattice-based cryptography is the use of conjectured hard problems on point lattices in Rn as the foundation for secure cryptographic systems. Attractive features of lattice cryptography include apparent resistance to quantum attacks (in contrast with most number-theoretic cryptography), high asymptotic efficiency and parallelism, security under worst-case intractability assumptions, and solutions to long-standing open problems in cryptography. This work surveys most of the major developments in lattice cryptography over the past ten years. The main focus is on the foundational short integer solution (SIS) and learning with errors (LWE) problems (and their more efficient ring-based variants), their provable hardness assuming the worst-case intractability of standard lattice problems, and their many cryptographic applications. C. Peikert. A Decade of Lattice Cryptography. Foundations and Trends  2014.
How to conduct systematic reviews more expeditiously?

Healthcare consumers, researchers, patients and policy makers increasingly use systematic reviews (SRs) to aid their decision-making process. However, the conduct of SRs can be a time-consuming and resource-intensive task. Often, clinical practice guideline developers or other decision-makers need to make informed decisions in a timely fashion (e.g. outbreaks of infection, hospital-based health technology assessments). Possible approaches to address the issue of timeliness in the production of SRs are to (a) implement process parallelisation, (b) adapt and apply innovative technologies, and/or (c) modify SR processes (e.g. study eligibility criteria, search sources, data extraction or quality assessment). Highly parallelised systematic reviewing requires substantial resources to support a team of experienced information specialists, reviewers and methodologists working alongside with clinical content experts to minimise the time for completing individual review steps while maximising the parallel progression of multiple steps. Effective coordination and management within the team and across external stakeholders are essential elements of this process. Emerging innovative technologies have a great potential for reducing workload and improving efficiency of SR production. The most promising areas of application would be to allow automation of specific SR tasks, in particular if these tasks are time consuming and resource intensive (e.g. language translation, study selection, data extraction). Modification of SR processes involves restricting, truncating and/or bypassing one or more SR steps, which may risk introducing bias to the review findings. Although the growing experiences in producing various types of rapid reviews (RR) and the accumulation of empirical studies exploring potential bias associated with specific SR tasks have contributed to the methodological development for expediting SR production, there is still a dearth of research examining the actual impact of methodological modifications and comparing the findings between RRs and SRs. This evidence would help to inform as to which SR tasks can be accelerated or truncated and to what degree, while maintaining the validity of review findings. Timely delivered SRs can be of value in informing healthcare decisions and recommendations, especially when there is practical urgency and there is no other relevant synthesised evidence.
Rumor Detection with Hierarchical Social Attention Network

Microblogs have become one of the most popular platforms for news sharing. However, due to its openness and lack of supervision, rumors could also be easily posted and propagated on social networks, which could cause huge panic and threat during its propagation. In this paper, we detect rumors by leveraging hierarchical representations at different levels and the social contexts. Specifically, we propose a novel hierarchical neural network combined with social information (HSA-BLSTM). We first build a hierarchical bidirectional long short-term memory model for representation learning. Then, the social contexts are incorporated into the network via attention mechanism, such that important semantic information is introduced to the framework for more robust rumor detection. Experimental results on two real world datasets demonstrate that the proposed method outperforms several state-of-the-arts in both rumor detection and early detection scenarios.
Visual object tracking using adaptive correlation filters

Although not commonly used, correlation filters can track complex objects through rotations, occlusions and other distractions at over 20 times the rate of current state-of-the-art techniques. The oldest and simplest correlation filters use simple templates and generally fail when applied to tracking. More modern approaches such as ASEF and UMACE perform better, but their training needs are poorly suited to tracking. Visual tracking requires robust filters to be trained from a single frame and dynamically adapted as the appearance of the target object changes. This paper presents a new type of correlation filter, a Minimum Output Sum of Squared Error (MOSSE) filter, which produces stable correlation filters when initialized using a single frame. A tracker based upon MOSSE filters is robust to variations in lighting, scale, pose, and nonrigid deformations while operating at 669 frames per second. Occlusion is detected based upon the peak-to-sidelobe ratio, which enables the tracker to pause and resume where it left off when the object reappears.
Studies on Ontology-based irrigation management information systems modeling

Research and development irrigation management information systems are the important measures of making irrigation management more modernized and standardized. The difficulties of building information systems have been increased along with the continuous development of information technology and the complexity of information systems, information systems put forward higher request to “shared” and “reuse”. Ontology-based information systems modeling can eliminate semantic differences, and carry out knowledge sharing and interoperability of different systems. In this paper, we introduce several common models which used in information systems modeling briefly; and then we introduce ontology, summarize ontology-based information systems modeling process; finally, we discuss the applications of ontology-based information systems modeling in irrigation management information systems preliminary.
SDN Architecture Impact on Network Security

The Software Defined Networking (SDN) paradigm introduces separation of data and control planes for flow-switched networks and enables different approaches to network security than those existing in present IP networks. The centralized control plane, i.e. the SDN controller, can host new security services that profit from the global view of the network and from direct control of switches. Some security services can be deployed as external applications that communicate with the controller. Due to the fact that all unknown traffic must be transmitted for investigation to the controller, maliciously crafted traffic can lead to Denial Of Service (DoS) attack on it. In this paper we analyse features of SDN in the context of security application. Additionally we point out some aspects of SDN networks that, if changed, could improve SDN network security capabilities. Moreover, the last section of the paper presents a detailed description of security application that detects a broad kind of malicious activity using key features of SDN architecture.
Citalopram therapy for depression: a review of 10 years of European experience and data from U.S. clinical trials.

This review summarizes and evaluates clinical experience with citalopram, the latest selective serotonin reuptake inhibitor (SSRI) to be approved for the treatment of depression in the United States.



Published reports of randomized, double-blind, controlled clinical studies of citalopram were retrieved using a MEDLINE literature search. Search terms included citalopram, SSRI, TCA (tricylic antidepressant), depression, and clinical. For each study, data on antidepressant efficacy and adverse events were evaluated. Pharmacokinetic studies and case reports were reviewed to supplement the evaluation of citalopram's safety and tolerability. Data presented at major medical conferences and published in abstract form also were reviewed.



Thirty randomized, double-blind, controlled studies of the antidepressant efficacy of citalopram were located and reviewed. In 11 studies, citalopram was compared with placebo (1 of these studies also included comparison with another SSRI). In 4 additional studies, the efficacy of citalopram in preventing depression relapse or recurrence was investigated. In another 11 studies (including 1 meta-analysis of published and unpublished trials), citalopram was compared with tricyclic and tetracyclic antidepressants. Finally, results are available from 4 studies in which citalopram was compared with other SSRIs. A placebo-controlled study of citalopram for the treatment of panic disorder was reviewed for data on long-term adverse events.



Data published over the last decade suggest that citalopram is (1) superior to placebo in the treatment of depression, (2) has efficacy similar to that of the tricyclic and tetracyclic antidepressants and to other SSRIs, and (3) is safe and well tolerated in the therapeutic dose range of 20 to 60 mg/day. Distinct from some other agents in its class, citalopram exhibits linear pharmacokinetics and minimal drug interaction potential. These features make citalopram an attractive agent for the treatment of depression, especially among the elderly and patients with comorbid illness.
(Self-Attentive) Autoencoder-based Universal Language Representation for Machine Translation

Universal language representation is the holy grail in machine translation (MT). Thanks to the new neural MT approach, it seems that there are good perspectives towards this goal. In this paper, we propose a new architecture based on combining variational autoencoders with encoder-decoders, and introducing an interlingual loss as an additional training objective. By adding and forcing this interlingual loss, we are able to train multiple encoders and decoders for each language, sharing a common universal representation. Since the final objective of this universal representation is producing close results for similar input sentences (in any language), we propose to evaluate it by encoding the same sentence in two different languages, decoding both latent representations into the same language and comparing both outputs. Preliminary results on the WMT 2017 Turkish/English task shows that the proposed architecture is capable of learning a universal language representation and simultaneously training both translation directions with state-of-the-art results.
Video Generation From Text

Generating videos from text has proven to be a significant challenge for existing generative models. We tackle this problem by training a conditional generative model to extract both static and dynamic information from text. This is manifested in a hybrid framework, employing a Variational Autoencoder (VAE) and a Generative Adversarial Network (GAN). The static features, called “gist,” are used to sketch text-conditioned background color and object layout structure. Dynamic features are considered by transforming input text into an image filter. To obtain a large amount of data for training the deep-learning model, we develop a method to automatically create a matched text-video corpus from publicly available online videos. Experimental results show that the proposed framework generates plausible and diverse short-duration smooth videos, while accurately reflecting the input text information. It significantly outperforms baseline models that directly adapt text-to-image generation procedures to produce videos. Performance is evaluated both visually and by adapting the inception score used to evaluate image generation in GANs.