nils-herrmann/clean_scirepeval huggingface dataset:
A stereo display prototype with multiple focal distances

Typical stereo displays provide incorrect focus cues because the light comes from a single surface. We describe a prototype stereo display comprising two independent fixed-viewpoint volumetric displays. Like autostereoscopic volumetric displays, fixed-viewpoint volumetric displays generate near-correct focus cues without tracking eye position, because light comes from sources at the correct focal distances. (In our prototype, from three image planes at different physical distances.) Unlike autostereoscopic volumetric displays, however, fixed-viewpoint volumetric displays retain the qualities of modern projective graphics: view-dependent lighting effects such as occlusion, specularity, and reflection are correctly depicted; modern graphics processor and 2-D display technology can be utilized; and realistic fields of view and depths of field can be implemented. While not a practical solution for general-purpose viewing, our prototype display is a proof of concept and a platform for ongoing vision research. The design, implementation, and verification of this stereo display are described, including a novel technique of filtering along visual lines using 1-D texture mapping.
Analyzing and Mining Comments and Comment Ratings on the Social Web

An analysis of the social video sharing platform YouTube and the news aggregator Yahoo! News reveals the presence of vast amounts of community feedback through comments for published videos and news stories, as well as through metaratings for these comments. This article presents an in-depth study of commenting and comment rating behavior on a sample of more than 10 million user comments on YouTube and Yahoo! News. In this study, comment ratings are considered first-class citizens. Their dependencies with textual content, thread structure of comments, and associated content (e.g., videos and their metadata) are analyzed to obtain a comprehensive understanding of the community commenting behavior. Furthermore, this article explores the applicability of machine learning and data mining to detect acceptance of comments by the community, comments likely to trigger discussions, controversial and polarizing content, and users exhibiting offensive commenting behavior. Results from this study have potential application in guiding the design of community-oriented online discussion platforms.
Reconciling mobile app privacy and usability on smartphones: could user privacy profiles help?

As they compete for developers, mobile app ecosystems have been exposing a growing number of APIs through their software development kits. Many of these APIs involve accessing sensitive functionality and/or user data and require approval by users. Android for instance allows developers to select from over 130 possible permissions. Expecting users to review and possibly adjust settings related to these permissions has proven unrealistic. In this paper, we report on the results of a study analyzing people's privacy preferences when it comes to granting permissions to different mobile apps. Our results suggest that, while people's mobile app privacy preferences are diverse, a relatively small number of profiles can be identified that offer the promise of significantly simplifying the decisions mobile users have to make. Specifically, our results are based on the analysis of settings of 4.8 million smartphone users of a mobile security and privacy platform. The platform relies on a rooted version of Android where users are allowed to choose between "granting", "denying" or "requesting to be dynamically prompted" when it comes to granting 12 different Android permissions to mobile apps they have downloaded.
Nutrition Therapy Recommendations for the Management of Adults With
Diabetes

There is no standard meal plan or eating pattern that works universally for all people with diabetes. In order to be effective, nutrition therapy should be individualized for each patient/client based on his or her individual health goals; personal and cultural preferences; health literacy and numeracy; access to healthful choices; and readiness, willingness, and ability to change. Nutrition interventions should emphasize a variety of minimally processed nutrient dense foods in appropriate portion sizes as part of a healthful eating pattern and provide the individual with diabetes with practical tools for day-to-day food plan and behavior change that can be maintained over the long term.
AppCaulk: Data Leak Prevention by Injecting Targeted Taint Tracking into Android Apps

As Android is entering the business domain, leaks of business-critical and personal information through apps become major threats. Due to the context-insensitive nature of the Android permission model, information flow policies cannot be enforced by on-board mechanisms. We therefore propose AppCaulk, an approach to harden any existing Android app by injecting a targeted dynamic taint analysis, which tracks and blocks unwanted information flows at runtime. Critical data flows are first discovered using a static taint analysis and the relevant data propagation paths are instrumented by a taint tracking code at register level. At runtime the dynamic taint analysis woven into the app detects and blocks data leaks as they are about to occur. In contrast to existing taint analysis approaches like Taint droid, AppCaulk does not require modification of the Android middleware and can thus be applied to any stock Android installation. In this paper, we explain the design of AppCaulk, describe the evaluation of its prototype, and compare its effectiveness with Taintdroid.
A novel wideband slotted mm wave microstrip patch antenna

In this paper, a novel approach for the design of a compact broadband planar microstrip patch antenna is presented for millimetre wave wireless applications. This antenna structure was realized on a Quartz crystal substrate of dielectric constant 3.8 with the thickness of 0.8mm with ground plane. In order to examine, the performances of this antenna it was designed at the frequency 30 GHz and simulated by IE3D software package of Zeland. This type of antenna is composed of two slots of different dimensions and fed by single coaxial feed. Appropriate design of the antenna structure resulted in three discontinuous resonant bands. The proposed antenna has a −10dB return loss impedance bandwidth of 10.3GHz. The performance of this antenna was verified and characterised in terms of the antenna return loss, radiation pattern, surface current distribution and power gain. Also, the numerical results show that the proposed antenna has good impedance bandwidth and radiation characteristics over the entire operating bands. Details of the proposed antenna configurations and design procedures are given.
Multiple-instance ranking: Learning to rank images for image retrieval

We study the problem of learning to rank images for image retrieval. For a noisy set of images indexed or tagged by the same keyword, we learn a ranking model from some training examples and then use the learned model to rank new images. Unlike previous work on image retrieval, which usually coarsely divide the images into relevant and irrelevant images and learn a binary classifier, we learn the ranking model from image pairs with preference relations. In addition to the relevance of images, we are further interested in what portion of the image is of interest to the user. Therefore, we consider images represented by sets of regions and propose multiple-instance rank learning based on the max margin framework. Three different schemes are designed to encode the multiple-instance assumption. We evaluate the performance of the multiple-instance ranking algorithms on real-word images collected from Flickr - a popular photo sharing service. The experimental results show that the proposed algorithms are capable of learning effective ranking models for image retrieval.
Systematic topology analysis and generation using degree correlations

Researchers have proposed a variety of metrics to measure important graph properties, for instance, in social, biological, and computer networks. Values for a particular graph metric may capture a graph's resilience to failure or its routing efficiency. Knowledge of appropriate metric values may influence the engineering of future topologies, repair strategies in the face of failure, and understanding of fundamental properties of existing networks. Unfortunately, there are typically no algorithms to generate graphs matching one or more proposed metrics and there is little understanding of the relationships among individual metrics or their applicability to different settings. We present a new, systematic approach for analyzing network topologies. We first introduce the dK-series of probability distributions specifying all degree correlations within d-sized subgraphs of a given graph G. Increasing values of d capture progressively more properties of G at the cost of more complex representation of the probability distribution. Using this series, we can quantitatively measure the distance between two graphs and construct random graphs that accurately reproduce virtually all metrics proposed in the literature. The nature of the dK-series implies that it will also capture any future metrics that may be proposed. Using our approach, we construct graphs for d=0, 1, 2, 3 and demonstrate that these graphs reproduce, with increasing accuracy, important properties of measured and modeled Internet topologies. We find that the d=2 case is sufficient for most practical purposes, while d=3 essentially reconstructs the Internet AS-and router-level topologies exactly. We hope that a systematic method to analyze and synthesize topologies offers a significant improvement to the set of tools available to network topology and protocol researchers.
Digital Enterprise Architecture - Transformation for the Internet of Things

Excellence in IT is both a driver and a key enabler of the digital transformation. The digital transformation changes the way we live, work, learn, communicate, and collaborate. The Internet of Things (IoT) fundamentally influences today's digital strategies with disruptive business operating models and fast changing markets. New business information systems are integrating emerging Internet of Things infrastructures and components. With the huge diversity of Internet of Things technologies and products organizations have to leverage and extend previous Enterprise Architecture efforts to enable business value by integrating Internet of Things architectures. Both architecture engineering and management of current information systems and business models are complex and currently integrating beside the Internet of Things synergistic subjects, like Enterprise Architecture in context with services & cloud computing, semantic-based decision support through ontologies and knowledge-based systems, big data management, as well as mobility and collaboration networks. To provide adequate decision support for complex business/IT environments, we have to make transparent the impact of business and IT changes over the integral landscape of affected architectural capabilities, like directly and transitively impacted IoT-objects, business categories, processes, applications, services, platforms and infrastructures. The paper describes a new metamodel-based approach for integrating Internet of Things architectural objects, which are semi-automatically federated into a holistic Digital Enterprise Architecture environment.
Wireless Fetal Monitoring Device with Provisions for Multiple Births

Cardiotocography is used to monitor the effect of contractions on fetal heart rate (FHR) and is used in the final trimester of pregnancy to detect fetal distress. This simple and efficient (patent-pending) architecture for Cardiotocography employs the Doppler technique to measure Fetal Heart Rate (FHR) and is scalable to any number of FHR monitors. The Doppler circuitry is designed for low cost and low power consumption to facilitate portability and compatibility with wireless data transmission. Measured results indicate performance consistent with conventional bedside systems, i.e. 8 bits of resolution with update rates =2 samples per second.
On Using Twitter to Monitor Political Sentiment and Predict Election Results

The body of content available on Twitter undoubtedly contains a diverse range of political insight and commentary. But, to what extent is this representative of an electorate? Can we model political sentiment effectively enough to capture the voting intentions of a nation during an election capaign? We use the recent Irish General Election as a case study for investigating the potential to model political sentiment through mining of social media. Our approach combines sentiment analysis using supervised learning and volume-based measures. We evaluate against the conventional election polls and the final election result. We find that social analytics using both volume-based measures and sentiment analysis are predictive and we make a number of observations related to the task of monitoring public sentiment during an election campaign, including examining a variety of sample sizes, time periods as well as methods for qualitatively exploring the underlying content.
Nitroglycerin: a review of its use in the treatment of vascular occlusion after soft tissue augmentation.

Skin necrosis after soft tissue augmentation with dermal fillers is a rare but potentially severe complication. Nitroglycerin paste may be an important treatment option for dermal and epidermal ischemia in cosmetic surgery.



To summarize the knowledge about nitroglycerin paste in cosmetic surgery and to understand its current use in the treatment of vascular compromise after soft tissue augmentation. To review the mechanism of action of nitroglycerin, examine its utility in the dermal vasculature in the setting of dermal filler-induced ischemia, and describe the facial anatomy danger zones in order to avoid vascular injury.



A literature review was conducted to examine the mechanism of action of nitroglycerin, and a treatment algorithm was proposed from clinical observations to define strategies for impending facial necrosis after filler injection.



Our experience with nitroglycerin paste and our review of the medical literature supports the use of nitroglycerin paste on the skin to help improve flow in the dermal vasculature because of its vasodilatory effect on small-caliber arterioles.
Secure unlocking of mobile touch screen devices by simple gestures: you can see it but you can not do it

With the rich functionalities and enhanced computing capabilities available on mobile computing devices with touch screens, users not only store sensitive information (such as credit card numbers) but also use privacy sensitive applications (such as online banking) on these devices, which make them hot targets for hackers and thieves. To protect private information, such devices typically lock themselves after a few minutes of inactivity and prompt a password/PIN/pattern screen when reactivated. Passwords/PINs/patterns based schemes are inherently vulnerable to shoulder surfing attacks and smudge attacks. Furthermore, passwords/PINs/patterns are inconvenient for users to enter frequently. In this paper, we propose GEAT, a gesture based user authentication scheme for the secure unlocking of touch screen devices. Unlike existing authentication schemes for touch screen devices, which use what user inputs as the authentication secret, GEAT authenticates users mainly based on how they input, using distinguishing features such as finger velocity, device acceleration, and stroke time. Even if attackers see what gesture a user performs, they cannot reproduce the behavior of the user doing gestures through shoulder surfing or smudge attacks. We implemented GEAT on Samsung Focus running Windows, collected 15009 gesture samples from 50 volunteers, and conducted real-world experiments to evaluate GEAT's performance. Experimental results show that our scheme achieves an average equal error rate of 0.5% with 3 gestures using only 25 training samples.
Accurate Prediction of Phase Transitions in Compressed Sensing via a Connection to Minimax Denoising

Compressed sensing posits that, within limits, one can undersample a sparse signal and yet reconstruct it accurately. Knowing the precise limits to such undersampling is important both for theory and practice. We present a formula that characterizes the allowed undersampling of generalized sparse objects. The formula applies to approximate message passing (AMP) algorithms for compressed sensing, which are here generalized to employ denoising operators besides the traditional scalar soft thresholding denoiser. This paper gives several examples including scalar denoisers not derived from convex penalization-the firm shrinkage nonlinearity and the minimax nonlinearity-and also nonscalar denoisers-block thresholding, monotone regression, and total variation minimization. Let the variables ε = k/N and δ = n/N denote the generalized sparsity and undersampling fractions for sampling the k-generalized-sparse N-vector x0 according to y=Ax0. Here, A is an n×N measurement matrix whose entries are iid standard Gaussian. The formula states that the phase transition curve δ = δ(ε) separating successful from unsuccessful reconstruction of x0 by AMP is given by δ = M(ε|Denoiser) where M(ε|Denoiser) denotes the per-coordinate minimax mean squared error (MSE) of the specified, optimally tuned denoiser in the directly observed problem y = x + z. In short, the phase transition of a noiseless undersampling problem is identical to the minimax MSE in a denoising problem. We prove that this formula follows from state evolution and present numerical results validating it in a wide range of settings. The above formula generates numerous new insights, both in the scalar and in the nonscalar cases.
Colorful Image Colorization

Given a grayscale photograph as input, this paper attacks the problem of hallucinating a plausible color version of the photograph. This problem is clearly underconstrained, so previous approaches have either relied on significant user interaction or resulted in desaturated colorizations. We propose a fully automatic approach that produces vibrant and realistic colorizations. We embrace the underlying uncertainty of the problem by posing it as a classification task and use class-rebalancing at training time to increase the diversity of colors in the result. The system is implemented as a feed-forward pass in a CNN at test time and is trained on over a million color images. We evaluate our algorithm using a “colorization Turing test,” asking human participants to choose between a generated and ground truth color image. Our method successfully fools humans on 32% of the trials, significantly higher than previous methods. Moreover, we show that colorization can be a powerful pretext task for self-supervised feature learning, acting as a cross-channel encoder. This approach results in state-of-the-art performance on several feature learning benchmarks.
Learning Distributed Word Representations For Bidirectional LSTM Recurrent Neural Network

Bidirectional long short-term memory (BLSTM) recurrent neural network (RNN) has been successfully applied in many tagging tasks. BLSTM-RNN relies on the distributed representation of words, which implies that the former can be futhermore improved through learning the latter better. In this work, we propose a novel approach to learn distributed word representations by training BLSTM-RNN on a specially designed task which only relies on unlabeled data. Our experimental results show that the proposed approach learns useful distributed word representations, as the trained representations significantly elevate the performance of BLSTM-RNN on three tagging tasks: part-ofspeech tagging, chunking and named entity recognition, surpassing word representations trained by other published methods.
MOBILE 3D MAPPING FOR SURVEYING EARTHWORK USING AN UNMANNED AERIAL VEHICLE (UAV)

Unmanned Aerial Vehicles (UAV) as a data acquisitio n platform and as a measurement instrument have bec ome attractive for many surveying applications in civil engineering. However, their performance is not wel l understood for these particular applications. The specific sco pe f the presented work is the performance evaluat ion of a UAV system that was built to rapidly acquire mobile 3D mapping data for large earthmoving construction sit es. Details to the components of the developed system (hardware an d co trol software) are explained. A novel program for photogrammetric flight planning and its execution f r the generation of 3D point clouds from digital m obile images is explained. A performance model for estimating th e position error was developed and tested in severa l r alistic construction environments. Results to these tests a re presented as they relate in particular to large excavation and earth moving construction sites. Results and experi ences with the developed UAV system are in particul ar seful for researchers or practitioners in need for successful ly adapting UAV technology for their application(s) .
On the Effectiveness of Ambient Sensing for Detecting NFC Relay Attacks

Smartphones with Near-Field Communication (NFC) may emulate contactless smart cards, which has resulted in the deployment of various access control, transportation and payment services, such as Google Pay and Apple Pay. Like contactless cards, however, NFC-based smartphone transactions are susceptible to relay attacks, and ambient sensing has been suggested as a potential countermeasure. In this study, we empirically evaluate the suitability of ambient sensors as a proximity detection mechanism for smartphone-based transactions under EMV constraints. We underpin our study using sensing data collected from 17 sensors from an emulated relay attack test-bed to assess whether they can thwart such attacks effectively. Each sensor, where feasible, was used to record 350-400 legitimate and relay (illegitimate) contactless transactions at two different physical locations. Our analysis provides an empirical foundation upon which to determine the efficacy of ambient sensing for providing a strong anti-relay mechanism in security-sensitive applications. We demonstrate that no single, evaluated mobile ambient sensor is suitable for such critical applications under realistic deployment constraints.