nils-herrmann/clean_scirepeval huggingface dataset:
Sleep Stage Classification Using EEG Signal Analysis: A Comprehensive Survey and New Investigation

Sleep specialists often conduct manual sleep stage scoring by visually inspecting the patient’s neurophysiological signals collected at sleep labs. This is, generally, a very difficult, tedious and time-consuming task. The limitations of manual sleep stage scoring have escalated the demand for developing Automatic Sleep Stage Classification (ASSC) systems. Sleep stage classification refers to identifying the various stages of sleep and is a critical step in an effort to assist physicians in the diagnosis and treatment of related sleep disorders. The aim of this paper is to survey the progress and challenges in various existing Electroencephalogram (EEG) signal-based methods used for sleep stage identification at each phase; including pre-processing, feature extraction and classification; in an attempt to find the research gaps and possibly introduce a reasonable solution. Many of the prior and current related studies use multiple EEG channels, and are based on 30 s or 20 s epoch lengths which affect the feasibility and speed of ASSC for real-time applications. Thus, in this paper, we also present a novel and efficient technique that can be implemented in an embedded hardware device to identify sleep stages using new statistical features applied to 10 s epochs of single-channel EEG signals. In this study, the PhysioNet Sleep European Data Format (EDF) Database was used. The proposed methodology achieves an average classification sensitivity, specificity and accuracy of 89.06%, 98.61% and 93.13%, respectively, when the decision tree classifier is applied. Finally, our new method is compared with those in recently published studies, which reiterates the high classification accuracy performance.
Phishing Training : A Preliminary Look at the Effects of Different Types of Training

In this paper, we present the preliminary results of an experiment conducted to observe the impact of the different training techniques to increase the likelihood of participants identifying and reporting phishing messages. Three different training approaches were used – general video/quiz training, just-in-time training with simulated phishing emails, and a leaderboard, which awarded users points for forwarding correct phishing messages and penalized them for incorrect ones. The experiment emulated a normal working day of an executive assistant of a manager in an organization. Each participant was expected to accomplish work tasks and respond to work-related emails while watching for and reporting phishing messages. We observed that both general training and the presence of a leaderboard decreased the propensity to click on a phishing message, while we found no effect for different types of just-in-time training.
Information security and privacy in healthcare: current state of research

Information security and privacy in the healthcare sector is an issue of growing importance. The adoption of digital patient records, increased regulation, provider consolidation and the increasing need for information exchange between patients, providers and payers, all point towards the need for better information security. We critically survey the literature on information security and privacy in healthcare, published in information systems journals as well as many other related disciplines including health informatics, public health, law, medicine, the trade press and industry reports. In this paper, we provide a holistic view of the recent research and suggest new areas of interest to the information systems community.
What do cMOOC participants talk about in social media?: a topic analysis of discourse in a cMOOC

Creating meaning from a wide variety of available information and being able to choose what to learn are highly relevant skills for learning in a connectivist setting. In this work, various approaches have been utilized to gain insights into learning processes occurring within a network of learners and understand the factors that shape learners' interests and the topics to which learners devote a significant attention. This study combines different methods to develop a scalable analytic approach for a comprehensive analysis of learners' discourse in a connectivist massive open online course (cMOOC). By linking techniques for semantic annotation and graph analysis with a qualitative analysis of learner-generated discourse, we examined how social media platforms (blogs, Twitter, and Facebook) and course recommendations influence content creation and topics discussed within a cMOOC. Our findings indicate that learners tend to focus on several prominent topics that emerge very quickly in the course. They maintain that focus, with some exceptions, throughout the course, regardless of readings suggested by the instructor. Moreover, the topics discussed across different social media differ, which can likely be attributed to the affordances of different media. Finally, our results indicate a relatively low level of cohesion in the topics discussed which might be an indicator of a diversity of the conceptual coverage discussed by the course participants.
Self-compassion is a better predictor than mindfulness of symptom severity and quality of life in mixed anxiety and depression.

Mindfulness has received considerable attention as a correlate of psychological well-being and potential mechanism for the success of mindfulness-based interventions (MBIs). Despite a common emphasis of mindfulness, at least in name, among MBIs, mindfulness proves difficult to assess, warranting consideration of other common components. Self-compassion, an important construct that relates to many of the theoretical and practical components of MBIs, may be an important predictor of psychological health. The present study compared ability of the Self-Compassion Scale (SCS) and the Mindful Attention Awareness Scale (MAAS) to predict anxiety, depression, worry, and quality of life in a large community sample seeking self-help for anxious distress (N = 504). Multivariate and univariate analyses showed that self-compassion is a robust predictor of symptom severity and quality of life, accounting for as much as ten times more unique variance in the dependent variables than mindfulness. Of particular predictive utility are the self-judgment and isolation subscales of the SCS. These findings suggest that self-compassion is a robust and important predictor of psychological health that may be an important component of MBIs for anxiety and depression.
Joint Action Unit localisation and intensity estimation through heatmap regression

This paper proposes a supervised learning approach to jointly perform facial Action Unit (AU) localisation and intensity estimation. Contrary to previous works that try to learn an unsupervised representation of the Action Unit regions, we propose to directly and jointly estimate all AU intensities through heatmap regression, along with the location in the face where they cause visible changes. Our approach aims to learn a pixel-wise regression function returning a score per AU, which indicates an AU intensity at a given spatial location. Heatmap regression then generates an image, or channel, per AU, in which each pixel indicates the corresponding AU intensity. To generate the ground-truth heatmaps for a target AU, the facial landmarks are first estimated, and a 2D Gaussian is drawn around the points where the AU is known to cause changes. The amplitude and size of the Gaussian is determined by the intensity of the AU. We show that using a single Hourglass network suffices to attain new state of the art results, demonstrating the effectiveness of such a simple approach. The use of heatmap regression allows learning of a shared representation between AUs without the need to rely on latent representations, as these are implicitly learned from the data. We validate the proposed approach on the BP4D dataset, showing a modest improvement on recent, complex, techniques, as well as robustness against misalignment errors. Code for testing and models will be available to download from https://github.com/ESanchezLozano/ Action-Units-Heatmaps.
The Social Media Intelligence Analyst for Emergency Management

The Social Media Intelligence Analyst is a new operational role within a State Control Centre in Victoria, Australia dedicated to obtaining situational awareness from social media to support decision making for emergency management. We outline where this role fits within the structure of a command and control organization, describe the requirements for such a position and detail the operational activities expected during an emergency event. As evidence of the importance of this role, we provide three real world examples where important information was obtained from social media which led to improved outcomes for the community concerned. This is the first time a dedicated role has been formally established solely for monitoring social media for emergency management intelligence gathering purposes in Victoria. To the best of our knowledge, it is also the first time such a dedicated position in an operational crisis coordination centre setting has been described in the literature.
Neural Architecture Search with Reinforcement Learning

Neural networks are powerful and flexible models that work well for many difficult learning tasks in image, speech and natural language understanding. Despite their success, neural networks are still hard to design. In this paper, we use a recurrent network to generate the model descriptions of neural networks and train this RNN with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set. On the CIFAR-10 dataset, our method, starting from scratch, can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy. Our CIFAR-10 model achieves a test error rate of 3.84, which is only 0.1 percent worse and 1.2x faster than the current state-of-the-art model. On the Penn Treebank dataset, our model can compose a novel recurrent cell that outperforms the widely-used LSTM cell, and other state-of-the-art baselines. Our cell achieves a test set perplexity of 62.4 on the Penn Treebank, which is 3.6 perplexity better than the previous state-ofthe-art.
Model Transformation: The Heart and Soul of Model-Driven Software Development

The motivation behind model-driven software development is to move the focus of work from programming to solution modeling. The model-driven approach has a potential to increase development productivity and quality by describing important aspects of a solution with more human-friendly abstractions and by generating common application fragments with templates. For this vision to become reality, software development tools need to automate the many tasks of model construction and transformation, including construction and transformation of models that can be round-trip engineered into code. In this article, we briefly examine different approaches to model transformation and offer recommendations on the desirable characteristics of a language for describing model transformations. In doing so, we are hoping to offer a measuring stick for judging the quality of future model transformation technologies.
Adversarial Manipulation of Deep Representations

We show that the image representations in a deep neural network (DNN) can be manipulated to mimic those of other natural images, with only minor, imperceptible perturbations to the original image. Previous methods for generating adversarial images focused on image perturbations designed to produce erroneous class labels. Here we instead concentrate on the internal layers of DNN representations, to produce a new class of adversarial images that differs qualitatively from others. While the adversary is perceptually similar to one image, its internal representation appears remarkably similar to a different image, from a different class and bearing little if any apparent similarity to the input. Further, they appear generic and consistent with the space of natural images. This phenomenon demonstrates the possibility to trick a DNN to confound almost any image with any other chosen image, and raises questions about DNN representations, as well as the properties of natural images themselves.
IoT-Based Prognostics and Systems Health Management for Industrial Applications

Prognostics and systems health management (PHM) is an enabling discipline that uses sensors to assess the health of systems, diagnoses anomalous behavior, and predicts the remaining useful performance over the life of the asset. The advent of the Internet of Things (IoT) enables PHM to be applied to all types of assets across all sectors, thereby creating a paradigm shift that is opening up significant new business opportunities. This paper introduces the concepts of PHM and discusses the opportunities provided by the IoT. Developments are illustrated with examples of innovations from manufacturing, consumer products, and infrastructure. From this review, a number of challenges that result from the rapid adoption of IoT-based PHM are identified. These include appropriate analytics, security, IoT platforms, sensor energy harvesting, IoT business models, and licensing approaches.
Visual Navigation for Mobile Robots

Looking at the number of living creatures using vision as their main sensor one should expect that vision also would be the first choice for mobile robots considering nice features as low price, low power, non contact and high potential information contents. Unfortunately it has proven much more difficult to extract the information from vision than expected and still no commercial robot relies on vision as its main sensor. In spite of this several successful methods have been developed. This chapter presents a number of visual methods that has been experimentally verified: artificial visual landmarks, corridor following using vanishing point, and road following using terrain classification based on data fusion of laser scanner and vision.
User Privacy in the Public Bitcoin Blockchain

Bitcoin is a peer-to-peer electronic cash system that maintains a public ledger with all transactions. The public availability of this information has implications for the privacy of the users. The public ledger consists of transactions that transfer funds from a set of inputs to a set of outputs. Both inputs and outputs are linked to Bitcoin addresses. In principle, the addresses are pseudonymous. In practice, it is sometimes possible to link Bitcoin addresses to real identities with the consequent privacy leaks. The possibilities of linking addresses to owners are multiplied when addresses are reused to receive funds multiple times. The reuse of addresses also multiplies the amount of private information that is leaked when an address is linked to a real identity. In this work we describe privacy-leaking effects of address reuse and gather statistics of address reuse in the Bitcoin network. We also describe collaborative (CoinJoin) transactions that prevent the privacy attacks that have been published in the literature. Then we analyze the Blockchain to find transactions that could potentially be CoinJoin transactions.
GERBIL - Benchmarking Named Entity Recognition and Linking consistently

The ability to compare systems from the same domain is of central importance for their introduction into complex applications. In the domains of named entity recognition and entity linking, the large number of systems and their orthogonal evaluation w.r.t. measures and datasets has led to an unclear landscape regarding the abilities and weaknesses of the different approaches. We present GERBIL—an improved platform for repeatable, storable and citable semantic annotation experiments— and its extension since being release. GERBIL has narrowed this evaluation gap by generating concise, archivable, humanand machine-readable experiments, analytics and diagnostics. The rationale behind our framework is to provide developers, end users and researchers with easy-to-use interfaces that allow for the agile, fine-grained and uniform evaluation of annotation tools on multiple datasets. By these means, we aim to ensure that both tool developers and end users can derive meaningful insights into the extension, integration and use of annotation applications. In particular, GERBIL provides comparable results to tool developers, simplifying the discovery of strengths and weaknesses of their implementations with respect to the state-of-the-art. With the permanent experiment URIs provided by our framework, we ensure the reproducibility and archiving of evaluation results. Moreover, the framework generates data in a machine-processable format, allowing for the efficient querying and postprocessing of evaluation results. Additionally, the tool diagnostics provided by GERBIL provide insights into the areas where tools need further refinement, thus allowing developers to create an informed agenda for extensions and end users to detect the right tools for their purposes. Finally, we implemented additional types of experiments including entity typing. GERBIL aims to become a focal point for the state-of-the-art, driving the research agenda of the community by presenting comparable objective evaluation results. Furthermore, we tackle the central problem of the evaluation of entity linking, i.e., we answer the question of how an evaluation algorithm can compare two URIs to each other without being bound to a specific knowledge base. Our approach to this problem opens a way to address the deprecation of URIs of existing gold standards for named entity recognition and entity linking, a feature which is currently not supported by the state-of-the-art. We derived the importance of this feature from usage and dataset requirements collected from the GERBIL user community, which has already carried out more than 24.000 single evaluations using our framework. Through the resulting updates, GERBIL now supports 8 tasks, 46 datasets and 20 systems.
The Internet of Things for Health Care: A Comprehensive Survey

The Internet of Things (IoT) makes smart objects the ultimate building blocks in the development of cyber-physical smart pervasive frameworks. The IoT has a variety of application domains, including health care. The IoT revolution is redesigning modern health care with promising technological, economic, and social prospects. This paper surveys advances in IoT-based health care technologies and reviews the state-of-the-art network architectures/platforms, applications, and industrial trends in IoT-based health care solutions. In addition, this paper analyzes distinct IoT security and privacy features, including security requirements, threat models, and attack taxonomies from the health care perspective. Further, this paper proposes an intelligent collaborative security model to minimize security risk; discusses how different innovations such as big data, ambient intelligence, and wearables can be leveraged in a health care context; addresses various IoT and eHealth policies and regulations across the world to determine how they can facilitate economies and societies in terms of sustainable development; and provides some avenues for future research on IoT-based health care based on a set of open issues and challenges.
Risk Uncertainty and Supply Chain Decisions: A Real Options Perspective

Supply chain risk uncertainty can create severe repercussions, thus it is not surprising that research interest in supply chain risk has been growing. While extant inquiry is informative, there is a lack of investigations that center on supply chain investment decisions when facing high levels of risk uncertainty. Given the potential dollar value involved in these decisions, an understanding of how these supply chain decisions are made is of significant theoretical and practical importance. Real options theory, with its focus on decision making under conditions of uncertainty, is an appealing theoretical lens for this endeavor. In essence, real options theory asserts that managerial decisions center on creating and then exercising or not exercising certain opportunities. To date, theorizing about and investigations of real options have used firms as their focus. Not yet examined are real options within supply chains that cross firm boundaries and drive much of the competitive activity in the modern economy. Accordingly, we extend real options theory to the supply chain context by examining how different types of options are approached relative to supply chain project investments. Specifically, we theorize how the options will be related to perceived value under conditions of high supply chain risk uncertainty. Overall, our investigation builds knowledge by extending real options theory to the supply chain context and by providing evidence suggesting some options operate differently in supply chains than they do in firms.  Postponement, Projects, Real Options Theory, Supply Chain Risk, Supply Chain Uncertainty, and Survey Research.
Cost-Benefit Analysis , the Death Penalty , and Rationales for Punishment

The paper contends that the use of cost-benefit analysis (CBA) for or against capital punishment is problematic insofar as CBA (1) commodifies and thus reduces the value of human life and (2) cannot quantify all costs and benefits. The paramount theories of punishment, retribution and utilitarianism, which are used as rationales for capital punishment, do not justify the use of cost-benefit analysis as part of that rationale. Calling on the theory of restorative justice, the paper recommends a change in the linguistic register used to describe the value of human beings. In particular, abolitionists should emphasize that human beings have essential value.  Advocates of the death penalty use economics to justify the use of capital punishment. Scott Turow, an Illinois-based lawyer says it well when he comments that two arguments frequently used by death penalty advocates are that, “the death penalty is a deterrent to others and it is more cost effective than keeping an individual in jail for life” (Turow). Edward Elijas takes the point further in writing the following, “Let’s imagine for a moment there was no death penalty. The only reasonable sentence would a life sentence. This would be costly to the tax payers, not only for the cost of housing and feeding the prisoner but because of the numerous appeals which wastes man hours and money. By treating criminals in this manner, we are encouraging behavior that will result in a prison sentence. If there is no threat of death to one who commits a murder, than that person is guaranteed to be provided with a decent living environment until their next parole hearing. They are definitely not getting the punishment they deserve” (http://www.cwrl.utexas.edu/). According to the argument, whether a person convicted