nils-herrmann/clean_scirepeval huggingface dataset:
Alarm fatigue: a patient safety concern.

Research has demonstrated that 72% to 99% of clinical alarms are false. The high number of false alarms has led to alarm fatigue. Alarm fatigue is sensory overload when clinicians are exposed to an excessive number of alarms, which can result in desensitization to alarms and missed alarms. Patient deaths have been attributed to alarm fatigue. Patient safety and regulatory agencies have focused on the issue of alarm fatigue, and it is a 2014 Joint Commission National Patient Safety Goal. Quality improvement projects have demonstrated that strategies such as daily electrocardiogram electrode changes, proper skin preparation, education, and customization of alarm parameters have been able to decrease the number of false alarms. These and other strategies need to be tested in rigorous clinical trials to determine whether they reduce alarm burden without compromising patient safety.
Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey

As a promising machine learning tool to handle the accurate pattern recognition from complex raw data, deep learning (DL) is becoming a powerful method to add intelligence to wireless networks with large-scale topology and complex radio conditions. DL uses many neural network layers to achieve a brain-like acute feature extraction from high-dimensional raw data. It can be used to find the network dynamics (such as hotspots, interference distribution, congestion points, traffic bottlenecks, spectrum availability, etc.) based on the analysis of a large amount of network parameters (such as delay, loss rate, link signal-to-noise ratio, etc.). Therefore, DL can analyze extremely complex wireless networks with many nodes and dynamic link quality. This paper performs a comprehensive survey of the applications of DL algorithms for different network layers, including physical layer modulation/coding, data link layer access control/resource allocation, and routing layer path search, and traffic balancing. The use of DL to enhance other network functions, such as network security, sensing data compression, etc., is also discussed. Moreover, the challenging unsolved research issues in this field are discussed in detail, which represent the future research trends of DL-based wireless networks. This paper can help the readers to deeply understand the state-of-the-art of the DL-based wireless network designs, and select interesting unsolved issues to pursue in their research.
Nonparametric Bayesian Word Sense Induction

We propose the use of a nonparametric Bayesian model, the Hierarchical Dirichlet Process (HDP), for the task of Word Sense Induction. Results are shown through comparison against Latent Dirichlet Allocation (LDA), a parametric Bayesian model employed by Brody and Lapata (2009) for this task. We find that the two models achieve similar levels of induction quality, while the HDP confers the advantage of automatically inducing a variable number of senses per word, as compared to manually fixing the number of senses a priori, as in LDA. This flexibility allows for the model to adapt to terms with greater or lesser polysemy, when evidenced by corpus distributional statistics. When trained on out-of-domain data, experimental results confirm the model’s ability to make use of a restricted set of topically coherent induced senses, when then applied in a restricted domain.
Reducing drift in visual odometry by inferring sun direction using a Bayesian Convolutional Neural Network

We present a method to incorporate global orientation information from the sun into a visual odometry pipeline using only the existing image stream, where the sun is typically not visible. We leverage recent advances in Bayesian Convolutional Neural Networks to train and implement a sun detection model that infers a three-dimensional sun direction vector from a single RGB image. Crucially, our method also computes a principled uncertainty associated with each prediction, using a Monte Carlo dropout scheme. We incorporate this uncertainty into a sliding window stereo visual odometry pipeline where accurate uncertainty estimates are critical for optimal data fusion. Our Bayesian sun detection model achieves a median error of approximately 12 degrees on the KITTI odometry benchmark training set, and yields improvements of up to 42% in translational ARMSE and 32% in rotational ARMSE compared to standard VO. An open source implementation of our Bayesian CNN sun estimator (Sun-BCNN) using Caffe is available at https://github.com/utiasSTARS/sun-bcnn-vo.
Big Data Pre-processing: A Quality Framework

With the abundance of raw data generated from various sources, Big Data has become a preeminent approach in acquiring, processing, and analyzing large amounts of heterogeneous data to derive valuable evidences. The size, speed, and formats in which data is generated and processed affect the overall quality of information. Therefore, Quality of Big Data (QBD) has become an important factor to ensure that the quality of data is maintained at all Big data processing phases. This paper addresses the QBD at the pre-processing phase, which includes sub-processes like cleansing, integration, filtering, and normalization. We propose a QBD model incorporating processes to support Data quality profile selection and adaptation. In addition, it tracks and registers on a data provenance repository the effect of every data transformation happened in the pre-processing phase. We evaluate the data quality selection module using large EEG dataset. The obtained results illustrate the importance of addressing QBD at an early phase of Big Data processing lifecycle since it significantly save on costs and perform accurate data analysis.
Noisy Networks for Exploration

We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent’s policy can be used to aid efficient exploration. The parameters of the noise are learned with gradient descent along with the remaining network weights. NoisyNet is straightforward to implement and adds little computational overhead. We find that replacing the conventional exploration heuristics for A3C, DQN and Dueling agents (entropy reward and -greedy respectively) with NoisyNet yields substantially higher scores for a wide range of Atari games, in some cases advancing the agent from sub to super-human performance.
Demystifying Incentives in the Consensus Computer

Cryptocurrencies like Bitcoin and the more recent Ethereum system allow users to specify scripts in transactions and contracts to support applications beyond simple cash transactions. In this work, we analyze the extent to which these systems can enforce the correct semantics of scripts. We show that when a script execution requires nontrivial computation effort, practical attacks exist which either waste miners' computational resources or lead miners to accept incorrect script results. These attacks drive miners to an ill-fated choice, which we call the verifier's dilemma, whereby rational miners are well-incentivized to accept unvalidated blockchains. We call the framework of computation through a scriptable cryptocurrency a consensus computer and develop a model that captures incentives for verifying computation in it. We propose a resolution to the verifier's dilemma which incentivizes correct execution of certain applications, including outsourced computation, where scripts require minimal time to verify. Finally we discuss two distinct, practical implementations of our consensus computer in real cryptocurrency networks like Ethereum.
Topographic Effects on the Seismic Response of Steep Slopes

A frequency-domain parametric study using generalized consistent transmitting boundaries has been performed to evaluate the significance of topographic effects on the seismic response of steep slopes. The results show that the peak amplification of motion at the crest of a slope occurs at a normalized frequency 1t/2 = 0.2, where H is the slope height and 2 is the wavelength of the motion. The importance of the natural site frequency is illustrated by the analysis of a stepped layer over a half-space. It was found that the natural frequency of the region behind the crest can dominate the response, relative to the topographic effect, for the conditions studied. Moreover, the effect of topography can be handled separately from the amplification due to the natural frequency of the deposit behind the crest of the slope. This concept of separating the amplification caused by topography from that caused by the natural frequency is advantageous to the development of a simplified method to estimate topographic effects.
Blockchain Engineering for the Internet of Things: Systems Security Perspective

The Internet of Things (IoT) technology has a potential to bring the benefits of intelligently interconnecting not just computers and humans, but most of everyday things. IoT has a promise of opening significant business process improvement opportunities leading to economic growth and cost reductions. However, there are many challenges facing IoT, including significant scalability and security challenges due to the integration of potentially huge number of things into the network. Many of scalability and security issues stem from a centralized, primarily client/server, architecture of IoT systems and frameworks. Blockchain technology, as a relativelly new approach to decentralized computation and assets management and transfer, has a potential to help solve a number of scalability and security issues that IoT is facing, primarilly through the removal of centralized points of failure for such systems. As such, blockchain technology and IoT integration provides a promising direction and it has recently generated significant research interest, e.g., .
 In this talk, we present our experiences based on our recent project in enhancing security and privacy in decentralized energy trading in smart grids using blockchain, multi-signatures and anonymous messaging streams , that has built upon our previous work on Bitcoin-based decentralized carbon emissions trading infrastructure model . In particular, we present the blockchain systems security issues within the context of IoT security and privacy requirements . This is done with the intention of producing an early integrated security model for blockchain-powered IoT systems . The presentation is constrained to the discussion of the architecture-level requirements [6]. Finally, we will present the main opportunity loss if the integration ignores the full realization of the real-world asset transaction paradigm.
42 Variability Bugs in the Linux Kernel: a Qualitative Analysis

Feature-sensitive verification pursues effective analysis of the exponentially many variants of a program family. However, researchers lack examples of concrete bugs induced by variability, occurring in real large-scale systems. Such a collection of bugs is a requirement for goal-oriented research, serving to evaluate tool implementations of feature-sensitive analyses by testing them on real bugs. We present a qualitative study of 42 variability bugs collected from bug-fixing commits to the Linux kernel repository. We analyze each of the bugs, and record the results in a database. In addition, we provide self-contained simplified C99 versions of the bugs, facilitating understanding and tool evaluation. Our study provides insights into the nature and occurrence of variability bugs in a large C software system, and shows in what ways variability affects and increases the complexity of software bugs.
Deep Learning for Encrypted Traffic Classification: An Overview

Traffic classification has been studied for two decades and applied to a wide range of applications from QoS provisioning and billing in ISPs to security-related applications in firewalls and intrusion detection systems. Port-based, data packet inspection, and classical machine learning methods have been used extensively in the past, but their accuracy have been declined due to the dramatic changes in the Internet traffic, particularly the increase in encrypted traffic. With the proliferation of deep learning methods, researchers have recently investigated these methods for traffic classification task and reported high accuracy. In this article, we introduce a general framework for deep-learning-based traffic classification. We present commonly used deep learning methods and their application in traffic classification tasks. Then, we discuss open problems and their challenges, as well as opportunities for traffic classification.
Accurate pedestrian localization in overhead depth images via Height-Augmented HOG

We tackle the challenge of reliably and automatically localizing pedestrians in real-life conditions through overhead depth imaging at unprecedented high-density conditions. Leveraging upon a combination of Histogram of Oriented Gradients-like feature descriptors, neural networks, data augmentation and custom data annotation strategies, this work contributes a robust and scalable machine learning-based localization algorithm, which delivers near-human localization performance in real-time, even with local pedestrian density of about 3 ped/m2, a case in which most stateof-the art algorithms degrade significantly in performance.
Comparametric equations with practical applications in quantigraphic image processing

It is argued that, hidden within the flow of signals from typical cameras, through image processing, to display media, is a homomorphic filter. While homomorphic filtering is often desirable, there are some occasions where it is not. Thus, cancellation of this implicit homomorphic filter is proposed, through the introduction of an antihomomorphic filter. This concept gives rise to the principle of quantigraphic image processing, wherein it is argued that most cameras can be modeled as an array of idealized light meters each linearly responsive to a semi-monotonic function of the quantity of light received, integrated over a fixed spectral response profile. This quantity depends only on the spectral response of the sensor elements in the camera. A particular class of functional equations, called comparametric equations, is introduced as a basis for quantigraphic image processing. These are fundamental to the analysis and processing of multiple images differing only in exposure. The "gamma correction" of an image is presented as a simple example of a comparametric equation, for which it is shown that the underlying quantigraphic function does not pass through the origin. Thus, it is argued that exposure adjustment by gamma correction is inherently flawed, and alternatives are provided. These alternatives, when applied to a plurality of images that differ only in exposure, give rise to a new kind of processing in the "amplitude domain". The theoretical framework presented in this paper is applicable to the processing of images from nearly all types of modern cameras. This paper is a much revised draft of a 1992 peer-reviewed but unpublished report by the author, entitled "Lightspace and the Wyckoff principle."
Deep Imitation Learning for Playing Real Time Strategy Games

Competitive Computer Games, such as StarCraft II, remain a largely unexplored and active application of Machine Learning, Artificial Intelligence, and Computer Vision. These games are highly complex as they typically 1) involve incomplete information, 2) include multiple strategies and elements that usually happen concurrently, and 3) run in real-time. For this project, we dive into a minigame for StarCraft II that involves many engagement skills such as focus fire, splitting, and kiting to win battles. This paper goes into the details of implementing an algorithm using behavioral cloning, a subset of imitation learning, to tackle the problem. Human expert replay data is used to train different systems that are evaluated on the minigame. Supervised learning, Convolutional Neural Networks, and Combined Loss Functions are all used in this project. While we have created an agent that shows some basic understanding of the game, the strategies performed are rather primitive. Nevertheless, this project establishes a useful framework that can be used for future expansion. (This project was completed in tandem with a related CS221 project.
What would users change in my app? summarizing app reviews for recommending software changes

Mobile app developers constantly monitor feedback in user reviews with the goal of improving their mobile apps and better meeting user expectations. Thus, automated approaches have been proposed in literature with the aim of reducing the effort required for analyzing feedback contained in user reviews via automatic classification/prioritization according to specific topics. In this paper, we introduce SURF (Summarizer of User Reviews Feedback), a novel approach to condense the enormous amount of information that developers of popular apps have to manage due to user feedback received on a daily basis. SURF relies on a conceptual model for capturing user needs useful for developers performing maintenance and evolution tasks. Then it uses sophisticated summarisation techniques for summarizing thousands of reviews and generating an interactive, structured and condensed agenda of recommended software changes. We performed an end-to-end evaluation of SURF on user reviews of 17 mobile apps (5 of them developed by Sony Mobile), involving 23 developers and researchers in total. Results demonstrate high accuracy of SURF in summarizing reviews and the usefulness of the recommended changes. In evaluating our approach we found that SURF helps developers in better understanding user needs, substantially reducing the time required by developers compared to manually analyzing user (change) requests and planning future software changes.
Charting the routes to revision : An interplay of writing goals , peer comments , and self-reflections from peer reviews

Building upon self-regulated learning theories, we examined the nature of student writing goals and the relationship of these writing goals to revision alone and in combination with two other important sources of students’ self-regulated revision—peer comments on their writing, and reflections for their own writing obtained from reviewing others’ writing. Data were obtained from a large introductory undergraduate class in the context of two 1000-word writing assignments involving online peer review and a required revision. We began with an investigation of students’ free response learning goals and a follow-up quantitative survey about the nature and structure of these writing goals. We found that: (a) students tended to create high-level substantive goals more often, (b) students change their writing goals across papers even for a very similar assignment, and (c) their writing goals divide into three dimensions: general writing goals, genre writing goals, and assignment goals. We then closely coded and analyzed the relative levels of association of revision changes with writing goals, peer comments, reflections from peer review, and combinations of these sources. Findings suggest that high-level revisions are commonly associated with writing goals, are especially likely to occur for combinations of the three sources, and peer comments alone appeared to make the largest contributions to revision.
Towards a Semantic Web of Things: A Hybrid Semantic Annotation, Extraction, and Reasoning Framework for Cyber-Physical System

Web of Things (WoT) facilitates the discovery and interoperability of Internet of Things (IoT) devices in a cyber-physical system (CPS). Moreover, a uniform knowledge representation of physical resources is quite necessary for further composition, collaboration, and decision-making process in CPS. Though several efforts have integrated semantics with WoT, such as knowledge engineering methods based on semantic sensor networks (SSN), it still could not represent the complex relationships between devices when dynamic composition and collaboration occur, and it totally depends on manual construction of a knowledge base with low scalability. In this paper, to addresses these limitations, we propose the semantic Web of Things (SWoT) framework for CPS (SWoT4CPS). SWoT4CPS provides a hybrid solution with both ontological engineering methods by extending SSN and machine learning methods based on an entity linking (EL) model. To testify to the feasibility and performance, we demonstrate the framework by implementing a temperature anomaly diagnosis and automatic control use case in a building automation system. Evaluation results on the EL method show that linking domain knowledge to DBpedia has a relative high accuracy and the time complexity is at a tolerant level. Advantages and disadvantages of SWoT4CPS with future work are also discussed.
Linguistic Reflections of Student Engagement in Massive Open Online Courses

While data from Massive Open Online Courses (MOOCs) offers the potential to gain new insights into the ways in which online communities can contribute to student learning, much of the richness of the data trace is still yet to be mined. In particular, very little work has attempted fine-grained content analyses of the student interactions in MOOCs. Survey research indicates the importance of student goals and intentions in keeping them involved in a MOOC over time. Automated fine-grained content analyses offer the potential to detect and monitor evidence of student engagement and how it relates to other aspects of their behavior. Ultimately these indicators reflect their commitment to remaining in the course. As a methodological contribution, in this paper we investigate using computational linguistic models to measure learner motivation and cognitive engagement from the text of forum posts. We validate our techniques using survival models that evaluate the predictive validity of these variables in connection with attrition over time. We conduct this evaluation in three MOOCs focusing on very different types of learning materials. Prior work demonstrates that participation in the discussion forums at all is a strong indicator of student commitment. Our methodology allows us to differentiate better among these students, and to identify danger signs that a struggling student is in need of support within a population whose interaction with the course offers the opportunity for effective support to be administered. Theoretical and practical implications will be discussed.
The NIST model for role-based access control: towards a unified standard

This paper describes a unified model for role-based access control (RBAC). RBAC is a proven technology for large-scale authorization. However, lack of a standard model results in uncertainty and confusion about its utility and meaning. The NIST model seeks to resolve this situation by unifying ideas from prior RBAC models, commercial products and research prototypes. It is intended to serve as a foundation for developing future standards. RBAC is a rich and open-ended technology which is evolving as users, researchers and vendors gain experience with it. The NIST model focuses on those aspects of RBAC for which consensus is available. It is organized into four levels of increasing functional capabilities called flat RBAC, hierarchical RBAC, constrained RBAC and symmetric RBAC. These levels are cumulative and each adds exactly one new requirement. An alternate approach comprising flat and hierarchical RBAC in an ordered sequence and two unordered features—constraints and symmetry—is also presented. The paper furthermore identifies important attributes of RBAC not included in the NIST model. Some are not suitable for inclusion in a consensus document. Others require further work and agreement before standardization is feasible.