nils-herrmann/clean_scirepeval huggingface dataset:
Fault Diagnosis in Enterprise Software Systems Using Discrete Monitoring Data

Success for many businesses depends on their information software systems. Keeping these systems operational is critical, as failure in these systems is costly. Such systems are in many cases sophisticated, distributed and dynamically composed. To ensure high availability and correct operation, it is essential that failures be detected promptly, their causes diagnosed and remedial actions taken. Although automated recovery approaches exists for specific problem domains, the problem-resolution process is in many cases manual and painstaking. Computer support personnel put a great deal of effort into resolving the reported failures. The growing size and complexity of these systems creates the need to automate this process. The primary focus of our research is on automated fault diagnosis and recovery using discrete monitoring data such as log files and notifications. Our goal is to quickly pinpoint the root-cause of a failure. Our contributions are: • Modelling discrete monitoring data for automated analysis, • Automatically leveraging common symptoms of failures from historic monitoring data using such models to pinpoint faults, and • Providing a model for decision-making under uncertainty such that appropriate recovery actions are chosen. Failures in such systems are caused by software defects, human error, hardware failures, environmental conditions and malicious behaviour. Our primary focus in this thesis is on software defects and misconfiguration.
Bayesian Foreground and Shadow Detection in Uncertain Frame Rate Surveillance Videos

In in this paper, we propose a new model regarding foreground and shadow detection in video sequences. The model works without detailed a priori object-shape information, and it is also appropriate for low and unstable frame rate video sources. Contribution is presented in three key issues: 1) we propose a novel adaptive shadow model, and show the improvements versus previous approaches in scenes with difficult lighting and coloring effects; 2) we give a novel description for the foreground based on spatial statistics of the neighboring pixel values, which enhances the detection of background or shadow-colored object parts; 3) we show how microstructure analysis can be used in the proposed framework as additional feature components improving the results. Finally, a Markov random field model is used to enhance the accuracy of the separation. We validate our method on outdoor and indoor sequences including real surveillance videos and well-known benchmark test sets.
Mindfulness, spirituality, and health-related symptoms.

Although the relationship between religious practice and health is well established, the relationship between spirituality and health is not as well studied. The objective of this study was to ascertain whether participation in the mindfulness-based stress reduction (MBSR) program was associated with increases in mindfulness and spirituality, and to examine the associations between mindfulness, spirituality, and medical and psychological symptoms.



Forty-four participants in the University of Massachusetts Medical School's MBSR program were assessed preprogram and postprogram on trait (Mindful Attention and Awareness Scale) and state (Toronto Mindfulness Scale) mindfulness, spirituality (, psychological distress, and reported medical symptoms. Participants also kept a log of daily home mindfulness practice. Mean changes in scores were computed, and relationships between changes in variables were examined using mixed-model linear regression.



There were significant improvements in spirituality, state and trait mindfulness, psychological distress, and reported medical symptoms. Increases in both state and trait mindfulness were associated with increases in spirituality. Increases in trait mindfulness and spirituality were associated with decreases in psychological distress and reported medical symptoms. Changes in both trait and state mindfulness were independently associated with changes in spirituality, but only changes in trait mindfulness and spirituality were associated with reductions in psychological distress and reported medical symptoms. No association was found between outcomes and home mindfulness practice.



Participation in the MBSR program appears to be associated with improvements in trait and state mindfulness, psychological distress, and medical symptoms. Improvements in trait mindfulness and spirituality appear, in turn, to be associated with improvements in psychological and medical symptoms.
HDPS: Heart disease prediction system

The diagnosis of heart disease in most cases depends on a complex combination of clinical and pathological data. Because of this complexity, there exists a significant amount of interest among clinical professionals and researchers regarding the efficient and accurate prediction of heart disease. In this paper, we develop a heart disease predict system that can assist medical professionals in predicting heart disease status based on the clinical data of patients. Our approaches include three steps. Firstly, we select 13 important clinical features, i.e., age, sex, chest pain type, trestbps, cholesterol, fasting blood sugar, resting ecg, max heart rate, exercise induced angina, old peak, slope, number of vessels colored, and thal. Secondly, we develop an artificial neural network algorithm for classifying heart disease based on these clinical features. The accuracy of prediction is near 80%. Finally, we develop a user-friendly heart disease predict system (HDPS). The HDPS system will be consisted of multiple features, including input clinical data section, ROC curve display section, and prediction performance display section (execute time, accuracy, sensitivity, specificity, and predict result). Our approaches are effective in predicting the heart disease of a patient. The HDPS system developed in this study is a novel approach that can be used in the classification of heart disease.
A security metric for structured security analysis of cyber-physical systems supporting SAE J3061

Complexity in modern vehicles has increased dramatically during the last years due to new features and applications. Modern vehicles are connected to the Internet as well as to other vehicles in close proximity and to the environment for different novel comfort services and safety-related applications. Enabling such services and applications requires wireless interfaces to the vehicle and therefore leads to open interfaces to the outside world. Attackers can use those interfaces to impair the privacy of the vehicle owner or to take control (of parts of) the vehicle, which strongly endangers the safety of the passengers as well as other road users. To avoid such attacks and to ensure the safety of modern vehicles, sophisticated structured processes and methods are needed. In this paper we propose a security metric to analyse cyberphysical systems (CPS) in a structured way. Its application leads to a secure system configuration with comparable as well as reusable results. Additionally, the security metric can be used to support the conceptual phase for the development of CPS specified in the new SAE security standard SAE J3061. A case study has been carried out to illustrate the application of the security metric.
ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

Machine learning (ML) has become a core component of many real-world applications and training data is a key factor that drives current progress. This huge success has led Internet companies to deploy machine learning as a service (MLaaS). Recently, the first membership inference attack has shown that extraction of information on the training set is possible in such MLaaS settings, which has severe security and privacy implications. However, the early demonstrations of the feasibility of such attacks have many assumptions on the adversary, such as using multiple so-called shadow models, knowledge of the target model structure, and having a dataset from the same distribution as the target model’s training data. We relax all these key assumptions, thereby showing that such attacks are very broadly applicable at low cost and thereby pose a more severe risk than previously thought. We present the most comprehensive study so far on this emerging and developing threat using eight diverse datasets which show the viability of the proposed attacks across domains. In addition, we propose the first effective defense mechanisms against such broader class of membership inference attacks that maintain a high level of utility of the ML model.
Performance of IEC 61850-9-2 Process Bus and Corrective Measure for Digital Relaying

International Electrotechnical Commission (IEC) standard 61850 proposes the Ethernet-based communication networks for protection and automation within the power substation. Major manufacturers are currently developing products for the process bus in compliance with IEC 61850 part 9-2. For the successful implementation of the IEC 61850-9-2 process bus, it is important to analyze the performance of time-critical messages for the substation protection and control functions. This paper presents the performance evaluation of the IEC 61850-9-2 process bus for a typical 345 kV/230 kV substation by studying the time-critical sampled value messages delay and loss by using the OPNET simulation tool in the first part of this paper. In the second part, this paper presents a corrective measure to address the issues with the several sampled value messages lost and/or delayed by proposing the sampled value estimation algorithm for any digital substation relaying. Finally, the proposed sampled value estimation algorithm has been examined for various power system scenarios with the help of PSCAD/EMTDC and MATLAB simulation tools.
Category learning and multiple memory systems

Categorization is a vitally important skill that people use every day. Early theories of category learning assumed a single learning system, but recent evidence suggests that human category learning may depend on many of the major memory systems that have been hypothesized by memory researchers. As different memory systems flourish under different conditions, an understanding of how categorization uses available memory systems will improve our understanding of a basic human skill, lead to better insights into the cognitive changes that result from a variety of neurological disorders, and suggest improvements in training procedures for complex categorization tasks.
All Else Being Equal Be Empowered

The classical approach to using utility functions suffers from the drawback of having to design and tweak the functions on a case by case basis. Inspired by examples from the animal kingdom, social sciences and games we propose empowerment, a rather universal function, defined as the information-theoretic capacity of an agent’s actuation channel. The concept applies to any sensorimotoric apparatus. Empowerment as a measure reflects the properties of the apparatus as long as they are observable due to the coupling of sensors and actuators via the environment.
Rethinking Data-Intensive Science Using Scalable Analytics Systems

"Next generation" data acquisition technologies are allowing scientists to collect exponentially more data at a lower cost. These trends are broadly impacting many scientific fields, including genomics, astronomy, and neuroscience. We can attack the problem caused by exponential data growth by applying horizontally scalable techniques from current analytics systems to accelerate scientific processing pipelines.
 In this paper, we describe ADAM, an example genomics pipeline that leverages the open-source Apache Spark and Parquet systems to achieve a 28x speedup over current genomics pipelines, while reducing cost by 63%. From building this system, we were able to distill a set of techniques for implementing scientific analyses efficiently using commodity "big data" systems. To demonstrate the generality of our architecture, we then implement a scalable astronomy image processing system which achieves a 2.8--8.9x improvement over the state-of-the-art MPI-based system.
From emotion perception to emotion experience: emotions evoked by pictures and classical music.

Most previous neurophysiological studies evoked emotions by presenting visual stimuli. Models of the emotion circuits in the brain have for the most part ignored emotions arising from musical stimuli. To our knowledge, this is the first emotion brain study which examined the influence of visual and musical stimuli on brain processing. Highly arousing pictures of the International Affective Picture System and classical musical excerpts were chosen to evoke the three basic emotions of happiness, sadness and fear. The emotional stimuli modalities were presented for 70 s either alone or combined (congruent) in a counterbalanced and random order. Electroencephalogram (EEG) Alpha-Power-Density, which is inversely related to neural electrical activity, in 30 scalp electrodes from 24 right-handed healthy female subjects, was recorded. In addition, heart rate (HR), skin conductance responses (SCR), respiration, temperature and psychometrical ratings were collected. Results showed that the experienced quality of the presented emotions was most accurate in the combined conditions, intermediate in the picture conditions and lowest in the sound conditions. Furthermore, both the psychometrical ratings and the physiological involvement measurements (SCR, HR, Respiration) were significantly increased in the combined and sound conditions compared to the picture conditions. Finally, repeated measures ANOVA revealed the largest Alpha-Power-Density for the sound conditions, intermediate for the picture conditions, and lowest for the combined conditions, indicating the strongest activation in the combined conditions in a distributed emotion and arousal network comprising frontal, temporal, parietal and occipital neural structures. Summing up, these findings demonstrate that music can markedly enhance the emotional experience evoked by affective pictures.
AdaBoost Learning for Human Detection Based on Histograms of Oriented Gradients

We developed a novel learning-based human detection system, which can detect people having different sizes and orientations, under a wide variety of backgrounds or even with crowds. To overcome the affects of geometric and rotational variations, the system automatically assigns the dominant orientations of each block-based feature encoding by using the rectangularand circulartype histograms of orientated gradients (HOG), which are insensitive to various lightings and noises at the outdoor environment. Moreover, this work demonstrated that Gaussian weight and tri-linear interpolation for HOG feature construction can increase detection performance. Particularly, a powerful feature selection algorithm, AdaBoost, is performed to automatically select a small set of discriminative HOG features with orientation information in order to achieve robust detection results. The overall computational time is further reduced significantly without any performance loss by using the cascade-ofrejecter structure, whose hyperplanes and weights of each stage are estimated by using the AdaBoost approach.
What are scoping studies? A review of the nursing literature.

Scoping studies are increasingly undertaken as distinct activities. The interpretation, methodology and expectations of scoping are highly variable. This suggests that conceptually, scoping is a poorly defined ambiguous term. The distinction between scoping as an integral preliminary process in the development of a research proposal or a formative, methodologically rigorous activity in its own right has not been extensively examined.



The aim of this review is to explore the nature and status of scoping studies within the nursing literature and develop a working definition to ensure consistency in the future use of scoping as a research related activity.



This paper follows an interpretative scoping review methodology.



An explicit systematic search strategy included literary and web-based key word searches and advice from key researchers. Electronic sources included bibliographic and national research register databases and a general browser.



The scoping studies varied widely in terms of intent, procedural and methodological rigor. An atheoretical stance was common although explicit conceptual clarification and development of a topic was limited. Four different levels of inquiry ranging from preliminary descriptive surveys to more substantive conceptual approaches were conceptualised. These levels reflected differing dimensional distinctions in which some activities constitute research whereas in others the scoping activities appear to fall outside the remit of research. Reconnaissance emerges as a common synthesising construct to explain the purpose of scoping.



Scoping studies in relation to nursing are embryonic and continue to evolve. Its main strengths lie in its ability to extract the essence of a diverse body of evidence giving it meaning and significance that is both developmental and intellectually creative. As with other approaches to research and evidence synthesis a more standardized approach is required.
Backstepping Approach for Controlling a Quadrotor Using Lagrange Form Dynamics

The dynamics of a quadrotor are a simplified form of helicopter dynamics that exhibit the same basic problems of underactuation, strong coupling, multi-input/multi-output design, and unknown nonlinearities. Control design for the quadrotor is more tractable yet reveals corresponding approaches for helicopter and UAV control design. In this paper, a backstepping approach is used for quadrotor controller design. In contrast to most other approaches, we apply backstepping on the Lagrangian form of the dynamics, not the state space form. This is complicated by the fact that the Lagrangian form for the position dynamics is bilinear in the controls. We confront this problem by using an inverse kinematics solution akin to that used in robotics. In addition, two neural nets are introduced to estimate the aerodynamic components, one for aerodynamic forces and one for aerodynamic moments. The result is a controller of intuitively appealing structure having an outer kinematics loop for position control and an inner dynamics loop for attitude control. The control approach described in this paper is robust since it explicitly deals with unmodeled state-dependent disturbances and forces without needing any prior knowledge of the same. A simulation study validates the results obtained in the paper. A. Das (B) · F. Lewis Automation and Robotics Research Institute, University of Texas at Arlington, 7300 Jack Newell Blvd. S., Fort Worth, TX 76118, USA e-mail: adas@arri.uta.edu F. Lewis e-mail: lewis@uta.edu K. Subbarao Department of Mechanical and Aerospace Engineering, University of Texas at Arlington, Arlington, TX 76019, USA e-mail: subbarao@uta.edu 128 J Intell Robot Syst (2009) 56:127–151
StackOverflow and GitHub: Associations between Software Development and Crowdsourced Knowledge

Stack Overflow is a popular on-line programming question and answer community providing its participants with rapid access to knowledge and expertise of their peers, especially benefitting coders. Despite the popularity of Stack Overflow, its role in the work cycle of open-source developers is yet to be understood: on the one hand, participation in it has the potential to increase the knowledge of individual developers thus improving and speeding up the development process. On the other hand, participation in Stack Overflow may interrupt the regular working rhythm of the developer, hence also possibly slow down the development process. In this paper we investigate the interplay between Stack Overflow activities and the development process, reflected by code changes committed to the largest social coding repository, GitHub. Our study shows that active GitHub committers ask fewer questions and provide more answers than others. Moreover, we observe that active Stack Overflow askers distribute their work in a less uniform way than developers that do not ask questions. Finally, we show that despite the interruptions incurred, the Stack Overflow activity rate correlates with the code changing activity in GitHub.
Explicit Model Predictive Control for trajectory tracking with mobile robots

In this paper the Model Predictive Control (MPC) strategy is used to solve the mobile robot trajectory tracking problem, where controller must ensure that robot follows pre-calculated trajectory. The so-called explicit optimal controller design and implementation are described. The MPC solution is calculated off-line and expressed as a piecewise affine function of the current state of a mobile robot. A linearized kinematic model of a differential drive mobile robot is used for the controller design purpose. The optimal controller, which has a form of a look-up table, is tested in simulation and experimentally.
Unique in the Crowd: The privacy bounds of human mobility

We study fifteen months of human mobility data for one and a half million individuals and find that human mobility traces are highly unique. In fact, in a dataset where the location of an individual is specified hourly, and with a spatial resolution equal to that given by the carrier's antennas, four spatio-temporal points are enough to uniquely identify 95% of the individuals. We coarsen the data spatially and temporally to find a formula for the uniqueness of human mobility traces given their resolution and the available outside information. This formula shows that the uniqueness of mobility traces decays approximately as the 1/10 power of their resolution. Hence, even coarse datasets provide little anonymity. These findings represent fundamental constraints to an individual's privacy and have important implications for the design of frameworks and institutions dedicated to protect the privacy of individuals.
VisDock: A Toolkit for Cross-Cutting Interactions in Visualization

Standard user applications provide a range of cross-cutting interaction techniques that are common to virtually all such tools: selection, filtering, navigation, layer management, and cut-and-paste. We present VisDock, a JavaScript mixin library that provides a core set of these cross-cutting interaction techniques for visualization, including selection (lasso, paths, shape selection, etc), layer management (visibility, transparency, set operations, etc), navigation (pan, zoom, overview, magnifying lenses, etc), and annotation (point-based, region-based, data-space based, etc). To showcase the utility of the library, we have released it as Open Source and integrated it with a large number of existing web-based visualizations. Furthermore, we have evaluated VisDock using qualitative studies with both developers utilizing the toolkit to build new web-based visualizations, as well as with end-users utilizing it to explore movie ratings data. Results from these studies highlight the usability and effectiveness of the toolkit from both developer and end-user perspectives.