nils-herrmann/clean_scirepeval huggingface dataset:
Lean product development flow

A general holistic framework, also called a process—named “Lean Product Development Flow (LPDF)”—for organizing the engineering work of Product Development (PD), has been proposed as a contribution to the emerging field of Lean Systems Engineering. The framework is based on Lean Principles, with emphasis on PD value-pulling workflow pulsed by takt periods. The value is defined as (1) mission assurance/product quality, (the traditional goals of Systems Engineering) and (2) reduced program cost and schedule achieved by a radical reduction of waste. LPDF is recommended for smaller design programs based on a high degree of legacy knowledge, with technologies mature enough so that the program feasibility is not in question. LPDF may involve limited-scope research, provided that it can be identified early in the program, and carried out separate from the main workflow. The paper is focused on aerospace and defense programs, which are presently burdened with as much as 60–90% of waste, but the process is also applicable to commercial programs. LPDF can be applied to the entire PD, to one or more milestones, and to a multilevel program. LPDF requires both detailed preparations and disciplined execution. The preparations include detailed Value Stream Mapping, separation of research from the main workflow, parsing of the Value Stream map into Takt Periods, architecting the LPDF team using dynamic allocation of resources, and team training. LPDF execution is organized as a flow through a series of short and equal work Takt Periods, each followed by an Integrative Event for structured, comprehensive coordination. Strategic and flexible tactical mitigations of uncertainties must be applied during the flow. LPDF also requires excellent leadership of a Chief Engineer, modeled after Toyota and Honda, who is a dedicated program “owner,” an expert systems designer, a strong leader focused on the program and product integrity, and skilled in consensus-building. The Chief Engineer is responsible for the entire program, with Assistant Chiefs assisting in selected technical areas, and a Project Manager assisting with program administration. An industrial pilot program is currently being undertaken to validate the method.
Quality indicators to detect pre-analytical errors in laboratory testing.

The identification of reliable quality indicators (QIs) is a crucial step in enabling users to quantify the quality of laboratory services. The current lack of attention to extra-laboratory factors is in stark contrast to the body of evidence pointing to the multitude of errors that continue to occur, particularly in the pre-analytical phase. The ISO 15189: 2012 standard for laboratory accreditation defines the pre-analytical phase, and recognizes the need to evaluate, monitor and improve all the procedures and processes in the initial phase of the testing cycle, including those performed in the phase of requesting tests and collecting samples, the so-called "pre-pre-analytical phase". Therefore, QIs should allow the identification of errors and non-conformities that can occur in all steps of the pre-analytical phase. Traditionally, pre-analytical errors are grouped into identification and sample problems. However, appropriate test requesting and complete request forms are now recognized as fundamental components in providing valuable laboratory services. The model of QIs developed by the Working Group of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) includes indicators related to both identification and sample problems as well as all other pre-analytical defects, including those in test requesting and request forms. It, moreover, provides the framework (with objective criteria) necessary for promoting the harmonization of available QIs in the pre-analytical phase.
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

For natural language understanding (NLU) technology to be maximally useful, it must be able to process language in a way that is not exclusively tailored to a specific task, genre, or dataset. In pursuit of this objective, we introduce the General Language Understanding Evaluation (GLUE) benchmark, a collection of tools for evaluating and analyzing the performance of models across a diverse set of existing NLU tasks. By including tasks with limited training data, GLUE is designed to favor and encourage models that share general linguistic knowledge across tasks. GLUE also includes a hand-crafted diagnostic test suite that enables detailed linguistic analysis of models. We evaluate baselines based on current methods for transfer and representation learning and find that multi-task training on all our tasks yields better results than training a separate model for each task. However, the low absolute performance of our best model indicates the need for improved general NLU systems.
A convolutional neural network-based model for knowledge base completion and its application to search personalization

In this paper, we propose a novel embedding model, named ConvKB, for knowledge base completion. Our model ConvKB advances state-of-the-art models by employing a convolutional neural network, so that it can capture global relationships and transitional characteristics between entities and relations in knowledge bases. In ConvKB, each triple (head entity, relation, tail entity) is represented as a 3-column matrix where each column vector represents a triple element. This 3-column matrix is then fed to a convolution layer where multiple filters are operated on the matrix to generate different feature maps. These feature maps are then concatenated into a single feature vector representing the input triple. The feature vector is multiplied with a weight vector via a dot product to return a score. This score is then used to predict whether the triple is valid or not. Experiments show that ConvKB obtains better link prediction and triple classification results than previous state-of-the-art models on benchmark datasets WN18RR, FB15k-237, WN11 and FB13. We further apply our ConvKB to a search personalization problem which aims to tailor the search results to each specific user based on the user’s personal interests and preferences. In particular, we model the potential relationship between the submitted query, the user and the search result (i.e., document) as a triple (query, user, document) on which the ConvKB is able to work. Experimental results on query logs from a commercial web search engine show that ConvKB achieves better performances than the standard ranker as well as strong search personalization baselines.
Web enabled spatio-temporal semantic analysis of traffic noise using CityGML

Indian population is growing very fast and is responsible for posing various environmental risks like traffic noise which is the primitive contributor to the overall noise pollution in urban environment. So, an attempt has been made to develop a web enabled application for spatio-temporal semantic analysis of traffic noise of one of the urban road segments in India. Initially, a traffic noise model was proposed for the study area based on the Calixto model. Later, a City Geographic Markup Language (CityGML) model, which is an OGC encoding standard for 3D data representation, was developed and stored into PostGIS. A web GIS framework was implemented for simulation of traffic noise level mapped on building walls using the data from PostGIS. Finally, spatio-temporal semantic analysis to quantify the effects in terms of threshold noise level, number of walls and roofs affected from start to the end of the day, was performed.
The Common Neural Basis of Autobiographical Memory, Prospection, Navigation, Theory of Mind, and the Default Mode: A Quantitative Meta-analysis

A core brain network has been proposed to underlie a number of different processes, including remembering, prospection, navigation, and theory of mind [Buckner, R. L., & Carroll, D. C. Self-projection and the brain. Trends in Cognitive Sciences, 11, 49–57, 2007]. This purported network—medial prefrontal, medial-temporal, and medial and lateral parietal regions—is similar to that observed during default-mode processing and has been argued to represent self-projection [Buckner, R. L., & Carroll, D. C. Self-projection and the brain. Trends in Cognitive Sciences, 11, 49–57, 2007] or scene-construction [Hassabis, D., & Maguire, E. A. Deconstructing episodic memory with construction. Trends in Cognitive Sciences, 11, 299–306, 2007]. To date, no systematic and quantitative demonstration of evidence for this common network has been presented. Using the activation likelihood estimation (ALE) approach, we conducted four separate quantitative meta-analyses of neuroimaging studies on: (a) autobiographical memory, (b) navigation, (c) theory of mind, and (d) default mode. A conjunction analysis between these domains demonstrated a high degree of correspondence. We compared these findings to a separate ALE analysis of prospection studies and found additional correspondence. Across all domains, and consistent with the proposed network, correspondence was found within the medial-temporal lobe, precuneus, posterior cingulate, retrosplenial cortex, and the temporo-parietal junction. Additionally, this study revealed that the core network extends to lateral prefrontal and occipital cortices. Autobiographical memory, prospection, theory of mind, and default mode demonstrated further reliable involvement of the medial prefrontal cortex and lateral temporal cortices. Autobiographical memory and theory of mind, previously studied as distinct, exhibited extensive functional overlap. These findings represent quantitative evidence for a core network underlying a variety of cognitive domains.
Cyberbullying: The Challenge to Define

Cyberbullying is a reality of the digital age. To address this phenomenon, it becomes imperative to understand exactly what cyberbullying is. Thus, establishing a workable and theoretically sound definition is essential. This article contributes to the existing literature in relation to the definition of cyberbullying. The specific elements of repetition, power imbalance, intention, and aggression, regarded as essential criteria of traditional face-to-face bullying, are considered in the cyber context. It is posited that the core bullying elements retain their importance and applicability in relation to cyberbullying. The element of repetition is in need of redefining, given the public nature of material in the online environment. In this article, a clear distinction between direct and indirect cyberbullying is made and a model definition of cyberbullying is offered. Overall, the analysis provided lends insight into how the essential bullying elements have evolved and should apply in our parallel cyber universe.
Research on leadership in a cross-cultural context : Making progress , and raising new questions

It is almost cliché to say that there has been an explosion in the amount of research on leadership in a cross-cultural context. In this review, we describe major advances and emerging patterns in this research domain over the last several years. Our starting point for this update is roughly 1996–1997, since those are the dates of two important reviews of the cross-cultural leadership literature [specifically, House, Wright, and Aditya (House, R. J., Wright, N. S., & Aditya, R. N. (1997). Cross-cultural research on organizational leadership: A critical analysis and a proposed theory. In: P. C. Earley, & M. Erez (Eds.), New perspectives on international industrial/organizational psychology (pp. 535–625). San Francisco, CA) and Dorfman (Dorfman, P. W. (1996). International and cross-cultural leadership research. ]. We describe the beginnings of the decline in the quest for universal leadership principles that apply equivalently across all cultures, and we focus on the increasing application of the dimensions of culture identified by Hofstede [Hofstede, G. (1980).  International differences in work-related values (Abridged ed.). Newbury Park, CA: Sage] and others to describe variation in leadership styles, practices, and preferences. We also note the emergence of the field of cross-cultural leadership as a legitimate and independent field of endeavor, as reflected in the emergence of publication outlets for this research, and the establishment of long-term multinational multi-investigator research programs on the topic. We conclude with a discussion of progress made since the two pieces that were our departure point, and of progress yet to be made. D 2003 Elsevier Inc. All rights reserved.
SYSTEMATIC GENERALIZATION: WHAT IS REQUIRED

Numerous models for grounded language understanding have been recently proposed, including (i) generic modules that can be used easily adapted to any given task with little adaptation and (ii) intuitively appealing modular models that require background knowledge to be instantiated. We compare generic and modular models in how much they lend themselves to a particular form of systematic generalization. Using a synthetic VQA test, we evaluate which models are capable of reasoning about all possible object pairs after training on only a small subset of them. Our findings show that the generalization of modular models is much more systematic and that it is highly sensitive to the module layout, i.e. to how exactly the modules are connected. We furthermore investigate if modular models that generalize well could be made more end-to-end by learning their layout and parametrization. We show how end-to-end methods from prior work often learn a wrong layout and a spurious parametrization that do not facilitate systematic generalization. Our results suggest that, in addition to modularity, systematic generalization in language understanding may require explicit regularizers or priors.
Bayesian Conditional Generative Adverserial Networks

Traditional GANs use a deterministic generator function (typically a neural network) to transform a random noise input z to a sample x that the discriminator seeks to distinguish. We propose a new GAN called Bayesian Conditional Generative Adversarial Networks (BC-GANs) that use a random generator function to transform a deterministic input y′ to a sample x. Our BC-GANs extend traditional GANs to a Bayesian framework, and naturally handle unsupervised learning, supervised learning, and semi-supervised learning problems. Experiments show that the proposed BC-GANs outperforms the state-of-the-arts.
Condition monitoring of wind turbines : Techniques and methods

Wind Turbines (WT) are one of the fastest growing sources of power production in the world today and there is a constant need to reduce the costs of operating and maintaining them. Condition monitoring (CM) is a tool commonly employed for the early detection of faults/failures so as to minimise downtime and maximize productivity. This paper provides a review of the state-of-the-art in the CM of wind turbines, describing the different maintenance strategies, CM techniques and methods, and highlighting in a table the various combinations of these that have been reported in the literature. Future research opportunities in fault diagnostics are identified using a qualitative fault tree analysis.
ChargeCache: Reducing DRAM latency by exploiting row access locality

DRAM latency continues to be a critical bottleneck for system performance. In this work, we develop a low-cost mechanism, called ChargeCache, that enables faster access to recently-accessed rows in DRAM, with no modifications to DRAM chips. Our mechanism is based on the key observation that a recently-accessed row has more charge and thus the following access to the same row can be performed faster. To exploit this observation, we propose to track the addresses of recently-accessed rows in a table in the memory controller. If a later DRAM request hits in that table, the memory controller uses lower timing parameters, leading to reduced DRAM latency. Row addresses are removed from the table after a specified duration to ensure rows that have leaked too much charge are not accessed with lower latency. We evaluate ChargeCache on a wide variety of workloads and show that it provides significant performance and energy benefits for both single-core and multi-core systems.
Learning Strict Identity Mappings in Deep Residual Networks

A family of super deep networks, referred to as residual networks or ResNet , achieved record-beating performance in various visual tasks such as image recognition, object detection, and semantic segmentation. The ability to train very deep networks naturally pushed the researchers to use enormous resources to achieve the best performance. Consequently, in many applications super deep residual networks were employed for just a marginal improvement in performance. In this paper, we propose âˆŠ-ResNet that allows us to automatically discard redundant layers, which produces responses that are smaller than a threshold âˆŠ, without any loss in performance. The âˆŠ-ResNet architecture can be achieved using a few additional rectified linear units in the original ResNet. Our method does not use any additional variables nor numerous trials like other hyperparameter optimization techniques. The layer selection is achieved using a single training process and the evaluation is performed on CIFAR-10, CIFAR-100, SVHN, and ImageNet datasets. In some instances, we achieve about 80% reduction in the number of parameters.
An All-In-One Convolutional Neural Network for Face Analysis

We present a multi-purpose algorithm for simultaneousface detection, face alignment, pose estimation, genderrecognition, smile detection, age estimation and face recognitionusing a single deep convolutional neural network (CNN). Theproposed method employs a multi-task learning framework thatregularizes the shared parameters of CNN and builds a synergyamong different domains and tasks. Extensive experimentsshow that the network has a better understanding of face andachieves state-of-the-art result for most of these tasks
Image Encryption Using Chaotic Based Artificial Neural Network

Cryptography is the science to transform the information in secure way. Encryption is best alternative to convert the data to be transferred to cipher data which is an unintelligible image or data which cannot be understood by any third person. Images are form of the multimedia data. There are many image encryption schemes already have been proposed, each one of them has its own potency and limitation. This paper presents a new algorithm for the image encryption/decryption scheme which has been proposed using chaotic neural network. Chaotic system produces the same results if the given inputs are same, it is unpredictable in the sense that it cannot be predicted in what way the system's behavior will change for any little change in the input to the system. The objective is to investigate the use of ANNs in the field of chaotic Cryptography. The weights of neural network are achieved based on chaotic sequence. The chaotic sequence generated and forwarded to ANN and weighs of ANN are updated which influence the generation of the key in the encryption algorithm. The algorithm has been implemented in the software tool MATLAB and results have been studied. To compare the relative performance peak signal to noise ratio (PSNR) and mean square error (MSE) are used.
Are adversarial examples inevitable?

A wide range of defenses have been proposed to harden neural networks against adversarial attacks. However, a pattern has emerged in which the majority of adversarial defenses are quickly broken by new attacks. Given the lack of success at generating robust defenses, we are led to ask a fundamental question: Are adversarial attacks inevitable? This paper analyzes adversarial examples from a theoretical perspective, and identifies fundamental bounds on the susceptibility of a classifier to adversarial attacks. We show that, for certain classes of problems, adversarial examples are inescapable. Using experiments, we explore the implications of theoretical guarantees for real-world problems and discuss how factors such as dimensionality and image complexity limit a classifier’s robustness against adversarial examples.
Sit-to-stand tests for COPD: A literature review.

Sit-to-stand tests (STST) have recently been developed as easy-to-use field tests to evaluate exercise tolerance in COPD patients. As several modalities of the test exist, this review presents a synthesis of the advantages and limitations of these tools with the objective of helping health professionals to identify the STST modality most appropriate for their patients.



Seventeen original articles dealing with STST in COPD patients have been identified and analysed including eleven on 1min-STST and four other versions of the test (ranging from 5 to 10 repetitions and from 30 s to 3 min). In these studies the results obtained in sit-to-stand tests and the recorded physiological variables have been correlated with the results reported in other functional tests.



A good set of correlations was achieved between STST performances and the results reported in other functional tests, as well as quality of life scores and prognostic index. According to the different STST versions the processes involved in performance are different and consistent with more or less pronounced associations with various physical qualities. These tests are easy to use in a home environment, with excellent metrological properties and responsiveness to pulmonary rehabilitation, even though repetition of the same movement remains a fragmented and restrictive approach to overall physical evaluation.



The STST appears to be a relevant and valid tool to assess functional status in COPD patients. While all versions of STST have been tested in COPD patients, they should not be considered as equivalent or interchangeable.
Protecting Internet users from becoming victimized attackers of click-fraud

Internet users are often victimized by malicious attackers. Some attackers infect and use innocent users’ machines to launch large-scale attacks without the users’ knowledge. One of such attacks is the click-fraud attack. Click-fraud happens in Pay-Per-Click (PPC) ad networks where the ad network charges advertisers for every click on their ads. Click-fraud has been proved to be a serious problem for the online advertisement industry. In a click-fraud attack, a user or an automated software clicks on an ad with a malicious intent and advertisers need to pay for those valueless clicks. Among many forms of click-fraud, botnets with the automated clickers are the most severe ones. In this paper, we present a method for detecting automated clickers from the user-side. The proposed method to Fight Click-Fraud, FCFraud, can be integrated into the desktop and smart device operating systems. Since most modern operating systems already provide some kind of anti-malware service, our proposed method can be implemented as a part of the service. We believe that an effective protection at the operating system level can save billions of dollars of the advertisers. Experiments show that FCFraud is 99.6% (98.2% in mobile ad library generated traffic) accurate in classifying ad requests from all user processes and it is 100% successful in detecting clickbots in both desktop and mobile devices. We implement a cloud backend for the FCFraud service to save battery power in mobile devices. The overhead of executing FCFraud is also analyzed and we show that it is reasonable for both the platforms.