nils-herrmann/clean_scirepeval huggingface dataset:
Residual-life distributions from component degradation signals : A Bayesian approach

Real-time condition monitoring is becoming an important tool in maintenance decision-making. Condition monitoring is the process of collecting real-time sensor information from a functioning device in order to reason about the health of the device. To make effective use of condition information, it is useful to characterize a device degradation signal, a quantity computed from condition information that captures the current state of the device and provides information on how that condition is likely to evolve in the future. If properly modeled, the degradation signal can be used to compute a residual-life distribution for the device being monitored, which can then be used in decision models. In this work, we develop Bayesian updating methods that use real-time condition monitoring information to update the stochastic parameters of exponential degradation models. We use these degradation models to develop a closed-form residual-life distribution for the monitored device. Finally, we apply these degradation and residual-life models to degradation signals obtained through the accelerated testing of bearings.
A Cold Start Recommendation System Using Item Correlation and User Similarity

Conventional recommendation systems tend to focus on variations of well-known information retrieval techniques. We took a fresh approach, rather than to follow the traditional, commonly applied recommendation methodology of creating a user-item matrix, and then using them to make recommendations. Instead, we established and examined three types of relationships: user-user similarity, wine-wine similarity and user preference relationships, in the form of adjacency lists. Using this approach, we did not encounter the usual problems associated with large dimension matrices, such as sparsity [5] and synonymy, as well as the basic problems of storing the large matrix, and having to perform a large number of computations every single time. We attempted to address the synonymy problem by using the wine-wine similarity index we formed. Also, we developed a model that took care of the cold start [6] problem which is fairly common in recommendation systems. We attempted to address the grey sheep problem as well, by minimizing the effect of any one outlying element, and taking the overall cumulative effect of all the elements. With our recommendation system, we wanted to establish a relationship of trust with the user, because just a few erroneous values would greatly discredit our models. In this paper, we defined 25 attributes by which a wine could be classified, and determined the wine-wine and useruser similarities, as well as the user preferences for the wines he has tried so far using our own novel scoring mechanism. The wines as such have had no scores associated with them with regards to the 25 attributes, rather, the scores were meaningful only when the relationships between the two wines were considered. We defined our own two methodologies, cold start and top k1, k2 models to address the issues associated with recommendation systems. Our experimental results showed that the newly introduced cold start model performed better than the traditional models of pure content-based [3], pure collaborative-based and content + colloborative based [10, 1] approaches with regards to Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. CSE 635 ’11 Buffalo, NY USA Copyright 20XX ACM X-XXXXX-XX-X/XX/XX ...$10.00. the quality. Also, our top k1, k2 model performed almost as well as the content + colloborative based model. Our resulting wine recommendations were consistent with the expectations of the user’s particular wine tastes.
GraphSig: A Scalable Approach to Mining Significant Subgraphs in Large Graph Databases

Graphs are being increasingly used to model a wide range of scientific data. Such widespread usage of graphs has generated considerable interest in mining patterns from graph databases. While an array of techniques exists to mine frequent patterns, we still lack a scalable approach to mine statistically significant patterns, specifically patterns with low p-values, that occur at low frequencies. We propose a highly scalable technique, called GraphSig, to mine significant subgraphs from large graph databases. We convert each graph into a set of feature vectors where each vector represents a region within the graph. Domain knowledge is used to select a meaningful feature set. Prior probabilities of features are computed empirically to evaluate statistical significance of patterns in the feature space. Following analysis in the feature space, only a small portion of the exponential search space is accessed for further analysis. This enables the use of existing frequent subgraph mining techniques to mine significant patterns in a scalable manner even when they are infrequent. Extensive experiments are carried out on the proposed techniques, and empirical results demonstrate that GraphSig is effective and efficient for mining significant patterns. To further demonstrate the power of significant patterns, we develop a classifier using patterns mined by GraphSig. Experimental results show that the proposed classifier achieves superior performance, both in terms of quality and computation cost, over state-of-the-art classifiers.
Application of the Laminar Navier-Stokes Equations for Solving 2D and 3D Pathfinding Problems with Static and Dynamic Spatial Constraints: Implementation and Validation in Comsol Multiphysics

Pathfinding problems consist in determining the optimal shortest path, or at least one path, between two points in the space. In this paper, we propose a particular approach, based on methods used in Computational Fluid Dynamics, that intends to solve such problems. In particular, we reformulate pathfinding problems as the motion of a viscous fluid via the use of the laminar Navier-Stokes equations completed with suitable boundary conditions corresponding to some characteristics of the considered problem: position of the initial and final points, a-priori information of the terrain, One-way routes and dynamic spatial configuration. The advantages of this technique, regarding existing ones (e.g., A* algorithm) is that it does not require a pre-processing method (e.g., graph conversion) of the environment and can manage complex geometries. Then, we propose a particular numerical implementation of this methodology by using Comsol Multiphysics (i.e., a modeling software based on Finite Element Methods). Finally, we validate our approach by considering several 2D and 3D benchmark cases. Results are compared with the ones returned by a simple A* algorithm. From those numerical tests, we deduce that our algorithms generate suitable routes (but not the shortest ones) for the studied problems in a computational time similar to the considered A*.
Azuma A Survey of Augmented Reality

This paper surveys the field of augmented reality (AR), in which 3D virtual objects are integrated into a 3D real environment in real time. It describes the medical, manufacturing , visualization, path planning, entertainment, and military applications that have been explored. This paper describes the characteristics of augmented reality systems, including a detailed discussion of the tradeoffs between optical and video blending approaches. Registration and sensing errors are two of the biggest problems in building effective augmented reality systems, so this paper summarizes current efforts to overcome these problems. Future directions and areas requiring further research are discussed. This survey provides a starting point for anyone interested in researching or using augmented reality.
ANNarchy: a code generation approach to neural simulations on parallel hardware

Many modern neural simulators focus on the simulation of networks of spiking neurons on parallel hardware. Another important framework in computational neuroscience, rate-coded neural networks, is mostly difficult or impossible to implement using these simulators. We present here the ANNarchy (Artificial Neural Networks architect) neural simulator, which allows to easily define and simulate rate-coded and spiking networks, as well as combinations of both. The interface in Python has been designed to be close to the PyNN interface, while the definition of neuron and synapse models can be specified using an equation-oriented mathematical description similar to the Brian neural simulator. This information is used to generate C++ code that will efficiently perform the simulation on the chosen parallel hardware (multi-core system or graphical processing unit). Several numerical methods are available to transform ordinary differential equations into an efficient C++code. We compare the parallel performance of the simulator to existing solutions.
A survey on virtual machine migration and server consolidation frameworks for cloud data centers

Modern Cloud Data Centers exploit virtualization for efficient resource management to reduce cloud computational cost and energy budget. Virtualization empowered by virtual machine (VM) migration meets the ever increasing demands of dynamic workload by relocating VMs within Cloud Data Centers. VM migration helps successfully achieve various resource management objectives such as load balancing, power management, fault tolerance, and system maintenance. However, being resourceintensive, the VM migration process rigorously affects application performance unless attended by smart optimization methods. Furthermore, a Cloud Data Centre exploits server consolidation and DVFS methods to optimize energy consumption. This paper reviews state-of-the-art bandwidth optimization schemes, server consolidation frameworks, DVFS-enabled power optimization, and storage optimization methods over WAN links. Through a meticulous literature review of state-of-the-art live VM migration schemes, thematic taxonomies are proposed to categorize the reported literature. The critical aspects of virtual machine migration schemes are investigated through a comprehensive analysis of the existing schemes. The commonalties and differences among existing VM migration schemes are highlighted through a set of parameters derived from the literature. Finally, open research issues and trends in the VM migration domain that necessitate further consideration to develop optimal VM migration schemes
Evaluation Datasets for Twitter Sentiment Analysis: A survey and a new dataset, the STS-Gold

Sentiment analysis over Twitter offers organisations and individuals a fast and effective way to monitor the publics’ feelings towards them and their competitors. To assess the performance of sentiment analysis methods over Twitter a small set of evaluation datasets have been released in the last few years. In this paper we present an overview of eight publicly available and manually annotated evaluation datasets for Twitter sentiment analysis. Based on this review, we show that a common limitation of most of these datasets, when assessing sentiment analysis at target (entity) level, is the lack of distinctive sentiment annotations among the tweets and the entities contained in them. For example, the tweet “I love iPhone, but I hate iPad” can be annotated with a mixed sentiment label, but the entity iPhone within this tweet should be annotated with a positive sentiment label. Aiming to overcome this limitation, and to complement current evaluation datasets, we present STS-Gold, a new evaluation dataset where tweets and targets (entities) are annotated individually and therefore may present different sentiment labels. This paper also provides a comparative study of the various datasets along several dimensions including: total number of tweets, vocabulary size and sparsity. We also investigate the pair-wise correlation among these dimensions as well as their correlations to the sentiment classification performance on different datasets.
ESPRIT-estimation of signal parameters via rotational invariance techniques

High-resolution signal parameter estimation is a problem of significance in many signal processing applications. Such applications include direction-of-arrival (DOA) estimation, system identification, and time series analysis. A novel approach to the general problem of signal parameter estimation is described. Although discussed in the context of direction-of-arrival estimation, ESPRIT can be applied to a wide variety of problems including accurate detection and estimation of sinusoids in noise. It exploits an underlying rotational invariance among signal subspaces induced by an array of sensors with a translational invariance structure. The technique, when applicable, manifests significant performance and computational advantages over previous algorithms such as MEM, Capon's MLM, and MUSIC.
Authorship Attribution for Social Media Forensics

The veil of anonymity provided by smartphones with pre-paid SIM cards, public Wi-Fi hotspots, and distributed networks like Tor has drastically complicated the task of identifying users of social media during forensic investigations. In some cases, the text of a single posted message will be the only clue to an author's identity. How can we accurately predict who that author might be when the message may never exceed 140 characters on a service like Twitter? For the past 50 years, linguists, computer scientists, and scholars of the humanities have been jointly developing automated methods to identify authors based on the style of their writing. All authors possess peculiarities of habit that influence the form and content of their written works. These characteristics can often be quantified and measured using machine learning algorithms. In this paper, we provide a comprehensive review of the methods of authorship attribution that can be applied to the problem of social media forensics. Furthermore, we examine emerging supervised learning-based methods that are effective for small sample sizes, and provide step-by-step explanations for several scalable approaches as instructional case studies for newcomers to the field. We argue that there is a significant need in forensics for new authorship attribution algorithms that can exploit context, can process multi-modal data, and are tolerant to incomplete knowledge of the space of all possible authors at training time.
Limiting, leaving, and (re)lapsing: an exploration of facebook non-use practices and experiences

Despite the abundance of research on social networking sites, relatively little research has studied those who choose not to use such sites. This paper presents results from a questionnaire of over 400 Internet users, focusing specifically on Facebook and those users who have left the service. Results show the lack of a clear, binary distinction between use and non-use, that various practices enable diverse ways and degrees of engagement with and disengagement from Facebook. Furthermore, qualitative analysis reveals numerous complex and interrelated motivations and justifications, both for leaving and for maintaining some type of connection. These motivations include: privacy, data misuse, productivity, banality, addiction, and external pressures. These results not only contribute to our understanding of online sociality by examining this under-explored area, but they also build on previous work to help advance how we conceptually account for the sociological processes of non-use.
An Information-Theoretic Framework for Flow Visualization

The process of visualization can be seen as a visual communication channel where the input to the channel is the raw data, and the output is the result of a visualization algorithm. From this point of view, we can evaluate the effectiveness of visualization by measuring how much information in the original data is being communicated through the visual communication channel. In this paper, we present an information-theoretic framework for flow visualization with a special focus on streamline generation. In our framework, a vector field is modeled as a distribution of directions from which Shannon's entropy is used to measure the information content in the field. The effectiveness of the streamlines displayed in visualization can be measured by first constructing a new distribution of vectors derived from the existing streamlines, and then comparing this distribution with that of the original data set using the conditional entropy. The conditional entropy between these two distributions indicates how much information in the original data remains hidden after the selected streamlines are displayed. The quality of the visualization can be improved by progressively introducing new streamlines until the conditional entropy converges to a small value. We describe the key components of our framework with detailed analysis, and show that the framework can effectively visualize 2D and 3D flow data.
WNGrad: Learn the Learning Rate in Gradient Descent

Adjusting the learning rate schedule in stochastic gradient methods is an important unresolved problem which requires tuning in practice. If certain parameters of the loss function such as smoothness or strong convexity constants are known, theoretical learning rate schedules can be applied. However, in practice, such parameters are not known, and the loss function of interest is not convex in any case. The recently proposed batch normalization reparametrization is widely adopted in most neural network architectures today because, among other advantages, it is robust to the choice of Lipschitz constant of the gradient in loss function, allowing one to set a large learning rate without worry. Inspired by batch normalization, we propose a general nonlinear update rule for the learning rate in batch and stochastic gradient descent so that the learning rate can be initialized at a high value, and is subsequently decreased according to gradient observations along the way. The proposed method is shown to achieve robustness to the relationship between the learning rate and the Lipschitz constant, and near-optimal convergence rates in both the batch and stochastic settings (O(1/T ) for smooth loss in the batch setting, and O(1/ √ T ) for convex loss in the stochastic setting). We also show through numerical evidence that such robustness of the proposed method extends to highly nonconvex and possibly non-smooth loss function in deep learning problems. Our analysis establishes some first theoretical understanding into the observed robustness for batch normalization and weight normalization.
AdaBoosting Neural Networks: Application to on-line Character Recognition

”Boosting” is a general method for improving the performanc e of any weak learning algorithm that consistently generates class ifiers which need to perform only slightly better than random guessing. A recently p roposed and very promising boosting algorithm isAdaBoost[4]. It has been applied with great success to several benchmark machine learning problems usi ng rather simple learning algorithms , in particular decision trees [1,2 ,5]. In this paper we use AdaBoost to improve the performances of a strong learning al gorithm: a neural network based on-line character recognition system. In par ticular we will show that it can be used to learn automatically a great variety of w riting styles even when the amount of training data for each style varies a lot. O ur system achieves about 1.4 % error on a handwritten digit data base of more than 200 writers.
Prediction of Battery Discharge and Estimation of Parasitic Loads for an Electric Aircraft

Predicting whether or not vehicle batteries contain sufficient charge to support operations over the remainder of a given flight plan is critical for electric aircraft. This paper describes an approach for identifying upper and lower uncertainty bounds on predictions that aircraft batteries will continue to meet output power and voltage requirements over the remainder of a flight plan. Battery discharge prediction is considered here in terms of the following components; (i) online battery state of charge estimation; (ii) prediction of future battery power demand as a function of an aircraft flight plan; (iii) online estimation of additional parasitic battery loads; and finally, (iv) estimation of flight plan safety. Substantial uncertainty is considered to be an irremovable part of the battery discharge prediction problem. However, highconfidence estimates of flight plan safety or lack of safety are shown to be generated from even highly uncertain prognostic predictions.
Enterprise Architecture design for ensuring strategic business IT alignment (integrating SAMM with TOGAF 9.1)

Strategic business IT (Information Technology) alignment is one of the main objectives that is achieved from the implementation of Enterprise Architecture (EA) in an organization. EA helps organizations to define architecture of business, information systems and technology that capable for aligning business strategy with IT organizations, through the development of business models, business strategy, business processes and organizations that aligned with infrastructure, applications and IT organizations. A good design of Enterprise Architecture should consider various viewpoints of IT alignment with the organization's business needs. This paper provides a solution how to design Enterprise Architecture which provides guarantee for strategic business IT alignment that is designed through the integration of SAMM component with TOGAF 9.1 metamodel.
Practical application of comfort theory in the perianesthesia setting.

Comfort management is a priority for patients in all settings. Comfort theory provides a foundational and holistic approach to comfort management. This article reviews comfort theory and presents the application of comfort theory in the perianesthesia setting.



-Based on the content of the following article, the reader will be able to (1) define comfort; (2) identify comfort interventions; and (3) discuss the importance of a goal for enhanced comfort in patients.
Social media for the emergency manager in disaster planning and response

This practitioner paper outlines some of the benefits for the use of social media, from the perspective of a locallevel or county-level emergency manager (EM). As compared to state and national level emergency management, because local level EMs have limited manpower and resources, social media can positively or negatively impact the effectiveness of communication before, during and after disaster strikes. Outlined in this paper are six key points where local EMs have specific needs that could be addressed by the effective use of social media and, in the opinion of the authors, represent the top issues that EMs face when considering how to leverage Twitter, Facebook, YouTube, Instagram and other social media platforms. The six needs addressed in this paper include: 1) Best practices for general social media use by EMs, 2) Social media use for internal command and control within the EM group, 3) Developing situation awareness by monitoring social media, especially prior to predicable events, 4) Communicating disaster preparedness messages through social media, 5) Using social media for gathering damage assessment information during, or immediately following a crisis, and 6) Leveraging social media volunteer groups. This short paper picks up where the Federal Emergency Management Agency’s social media training leaves off and attempts to represent these six needs as use cases for researchers and developers to address in future publications and products.