nils-herrmann/clean_scirepeval huggingface dataset:
Customer Journey a method to investigate user experience.

Workplaces are stages for work experiences. There is a need to understand the experience as a definitive factor in workplace management. However the ways to investigate the experience are many and there are many perspectives to approach the methods. This paper aims to answer the question how to asses the work environments from the user perspective, as part of user experience? The methods for user orientated workplace management are presented. The conclusions indicate the different objects of different methods, which all together provide rich data for workplace management, user organisation and other stakeholders – for the ones creating user experiences.
Intelligent travel chatbot for predictive recommendation in echo platform

Chatbot is a computer application that interacts with users using natural language in a similar way to imitate a human travel agent. A successful implementation of a chatbot system can analyze user preferences and predict collective intelligence. In most cases, it can provide better user-centric recommendations. Hence, the chatbot is becoming an integral part of the future consumer services. This paper is an implementation of an intelligent chatbot system in travel domain on Echo platform which would gather user preferences and model collective user knowledge base and recommend using the Restricted Boltzmann Machine (RBM) with Collaborative Filtering. With this chatbot based on DNN, we can improve human to machine interaction in the travel domain.
Multidimensional Binary Search Trees Used for Associative Searching

This paper develops the multidimensional binary search tree (or <italic>k</italic>-d tree, where <italic>k</italic> is the dimensionality of the search space) as a data structure for storage of information to be retrieved by associative searches. The <italic>k</italic>-d tree is defined and examples are given. It is shown to be quite efficient in its storage requirements. A significant advantage of this structure is that a single data structure can handle many types of queries very efficiently. Various utility algorithms are developed; their proven average running times in an <italic>n</italic> record file are: insertion, <italic>O</italic>(log <italic>n</italic>); deletion of the root, <italic>O</italic>(<italic>n</italic><supscrpt>(<italic>k</italic>-1)/<italic>k</italic></supscrpt>); deletion of a random node, <italic>O</italic>(log <italic>n</italic>); and optimization (guarantees logarithmic performance of searches), <italic>O</italic>(<italic>n</italic> log <italic>n</italic>). Search algorithms are given for partial match queries with <italic>t</italic> keys specified [proven maximum running time of <italic>O</italic>(<italic>n</italic><supscrpt>(<italic>k</italic>-<italic>t</italic>)/<italic>k</italic></supscrpt>)] and for nearest neighbor queries [empirically observed average running time of <italic>O</italic>(log <italic>n</italic>).] These performances far surpass the best currently known algorithms for these tasks. An algorithm is presented to handle any general intersection query. The main focus of this paper is theoretical. It is felt, however, that <italic>k</italic>-d trees could be quite useful in many applications, and examples of potential uses are given.
Approaches to reflexivity in qualitative research.

Reflexivity is a curious term with various meanings. Finding a definition of reflexivity that demonstrates what it means and how it is achieved is difficult (Colbourne and Sque 2004). Moreover, writings on reflexivity have not been transparent in terms of the difficulties, practicalities and methods of the process (Mauthner and Doucet 2003). Nevertheless, it is argued that an attempt be made to gain ‘some kind of intellectual handle’ on reflexivity in order to make use of it as a guiding standard (Freshwater and Rolfe 2001). The role of reflexivity in the many and varied qualitative methodologies is significant. It is therefore a concept of particular relevance to nursing as qualitative methodologies play a principal function in nursing enquiry. Reflexivity assumes a pivotal role in feminist research (King 1994). It is also paramount in participatory action research (Robertson 2000), ethnographies, and hermeneutic and post-structural approaches (Koch and Harrington 1998). Furthermore, it plays an integral part in medical case study research reflexivity epistemological critical feminist ▲ ▲ ▲ ▲ k e y w o rd s
Microbial cellulose utilization: fundamentals and biotechnology.

Fundamental features of microbial cellulose utilization are examined at successively higher levels of aggregation encompassing the structure and composition of cellulosic biomass, taxonomic diversity, cellulase enzyme systems, molecular biology of cellulase enzymes, physiology of cellulolytic microorganisms, ecological aspects of cellulase-degrading communities, and rate-limiting factors in nature. The methodological basis for studying microbial cellulose utilization is considered relative to quantification of cells and enzymes in the presence of solid substrates as well as apparatus and analysis for cellulose-grown continuous cultures. Quantitative description of cellulose hydrolysis is addressed with respect to adsorption of cellulase enzymes, rates of enzymatic hydrolysis, bioenergetics of microbial cellulose utilization, kinetics of microbial cellulose utilization, and contrasting features compared to soluble substrate kinetics. A biological perspective on processing cellulosic biomass is presented, including features of pretreated substrates and alternative process configurations. Organism development is considered for "consolidated bioprocessing" (CBP), in which the production of cellulolytic enzymes, hydrolysis of biomass, and fermentation of resulting sugars to desired products occur in one step. Two organism development strategies for CBP are examined: (i) improve product yield and tolerance in microorganisms able to utilize cellulose, or (ii) express a heterologous system for cellulose hydrolysis and utilization in microorganisms that exhibit high product yield and tolerance. A concluding discussion identifies unresolved issues pertaining to microbial cellulose utilization, suggests approaches by which such issues might be resolved, and contrasts a microbially oriented cellulose hydrolysis paradigm to the more conventional enzymatically oriented paradigm in both fundamental and applied contexts.
Randomized controlled trials: part 17 of a series on evaluation of scientific publications.

In clinical research, randomized controlled trials (RCTs) are the best way to study the safety and efficacy of new treatments. RCTs are used to answer patient-related questions and are required by governmental regulatory bodies as the basis for approval decisions.



To help readers understand and evaluate RCTs, we discuss the methods and qualitative requirements of RCTs with reference to the literature and an illustrative case study. The discussion here corresponds to expositions of the subject that can be found in many textbooks but also reflects the authors' personal experience in planning, conducting and analyzing RCTs.



The quality of an RCT depends on an appropriate study question and study design, the prevention of systematic errors, and the use of proper analytical techniques. All of these aspects must be attended to in the planning, conductance, analysis, and reporting of RCTs. RCTs must also meet ethical and legal requirements.



RCTs cannot yield reliable data unless they are planned, conducted, analyzed, and reported in ways that are methodologically sound and appropriate to the question being asked. The quality of any RCT must be critically evaluated before its relevance to patient care can be considered.
Power system stability analysis and control based on PMU

In allusion to the “on-line beforehand decision-making, real time matching”, this paper proposes the stability control flow based on PMU for interconnected power system, which is a real-time stability control. In this scheme, preventive control, emergency control and corrective control are designed to a closed-loop rolling control process, it will protect the stability of power system. Then it ameliorates the corrective control process, and presents a new control method which is based on PMU and EEAC method. This scheme can ensure the real-time quality and advance the veracity for the corrective control.
Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detection

Aircraft detection from very high resolution (VHR) remote sensing images has been drawing increasing interest in recent years due to the successful civil and military applications. However, several challenges still exist: 1) extracting the high-level features and the hierarchical feature representations of the objects is difficult; 2) manual annotation of the objects in large image sets is generally expensive and sometimes unreliable; and 3) locating objects within such a large image is difficult and time consuming. In this paper, we propose a weakly supervised learning framework based on coupled convolutional neural networks (CNNs) for aircraft detection, which can simultaneously solve these problems. We first develop a CNN-based method to extract the high-level features and the hierarchical feature representations of the objects. We then employ an iterative weakly supervised learning framework to automatically mine and augment the training data set from the original image. We propose a coupled CNN method, which combines a candidate region proposal network and a localization network to extract the proposals and simultaneously locate the aircraft, which is more efficient and accurate, even in large-scale VHR images. In the experiments, the proposed method was applied to three challenging high-resolution data sets: the Sydney International Airport data set, the Tokyo Haneda Airport data set, and the Berlin Tegel Airport data set. The extensive experimental results confirm that the proposed method can achieve a higher detection accuracy than the other methods.
MANAGING GRADUATE UNEMPLOYMENT IN EMERGING ECONOMIES : CRITICAL ANALYSIS OF THE SKILLS MISMATCH AND OVERSUPPLY THESES

Graduate unemployment and its management are challenges that leaders of the economy, managers and policy analysts grapple with on a daily basis. As a result, economic leaders and managers of economies have sought theoretical explanations to guide their management strategies of graduate unemployment. Th ere are two competing theses to explain the problem: skills mismatch and skills oversupply. However, due to the seeming simplicity of basic tenets and policy implications of the skills mismatch thesis, many governments and laypersons have blamed graduate unemployment on it. Th is paper argues that policy solutions based entirely on skills mismatch may trigger another form of unemployment, oversupply of skilled graduates. Furthermore, oversupply of graduates is more likely to be the signifi cant cause of graduate unemployment than skills mismatch. An eff ective policy, therefore, is one that takes into account interventions to stimulate demand for labor while at the same time manages the supply of skilled labor. Such an approach will provide more sustainable solutions to graduate unemployment. In addition, the potential contributions of psychologists in the eff orts towards the management of graduate unemployment are also outlined.
Improving Regression Performance with Distributional Losses

There is growing evidence that converting targets to soft targets in supervised learning can provide considerable gains in performance. Much of this work has considered classification, converting hard zero-one values to soft labels—such as by adding label noise, incorporating label ambiguity or using distillation. In parallel, there is some evidence from a regression setting in reinforcement learning that learning distributions can improve performance. In this work, we investigate the reasons for this improvement, in a regression setting. We introduce a novel distributional regression loss, and similarly find it significantly improves prediction accuracy. We investigate several common hypotheses, around reducing overfitting and improved representations. We instead find evidence for an alternative hypothesis: this loss is easier to optimize, with better behaved gradients, resulting in improved generalization. We provide theoretical support for this alternative hypothesis, by characterizing the norm of the gradients of this loss.
Firefly Algorithm: Recent Advances and Applications

Nature-inspired metaheuristic algorithms, especially those based on swarm intelligence, have attracted much attention in the last ten years. Firefly algorithm appeared in about five years ago, its literature has expanded dramatically with diverse applications. In this paper, we will briefly review the fundamentals of firefly algorithm together with a selection of recent publications. Then, we discuss the optimality associated with balancing exploration and exploitation, which is essential for all metaheuristic algorithms. By comparing with intermittent search strategy, we conclude that metaheuristics such as firefly algorithm are better than the optimal intermittent search strategy. We also analyse algorithms and their implications for higher-dimensional optimization problems.
Parkinsonism: upturned walking stick as an aid to locomotion.

Three parkinsonian patients are described who independently discovered that their gait was facilitated by inverting a walking stick and using the handle, carried a few inches from the ground, as a visual cue or target to step over and initiate walking. It is suggested that the "inverted" walking stick have wider application in patients with Parkinson's disease as an aid to walking, particularly if they have difficulty with step initiation and maintenance of stride length.
DIAG-NRE: A Deep Pattern Diagnosis Framework for Distant Supervision Neural Relation Extraction

Modern neural network models have achieved the state-ofthe-art performance on relation extraction (RE) tasks. Although distant supervision (DS) can automatically generate training labels for RE, the effectiveness of DS highly depends on datasets and relation types, and sometimes it may introduce large labeling noises. In this paper, we propose a deep pattern diagnosis framework, DIAG-NRE, that aims to diagnose and improve neural relation extraction (NRE) models trained on DS-generated data. DIAG-NRE includes three stages: (1) The deep pattern extraction stage employs reinforcement learning to extract regular-expression-style patterns from NRE models. (2) The pattern refinement stage builds a pattern hierarchy to find the most representative patterns and lets human reviewers evaluate them quantitatively by annotating a certain number of pattern-matched examples. In this way, we minimize both the number of labels to annotate and the difficulty of writing heuristic patterns. (3) The weak label fusion stage fuses multiple weak label sources, including DS and refined patterns, to produce noise-reduced labels that can train a better NRE model. To demonstrate the broad applicability of DIAG-NRE, we use it to diagnose 14 relation types of two public datasets with one simple hyperparameter configuration. We observe different noise behaviors and obtain significant F1 improvements on all relation types suffering from large labeling noises.
Beyond journalism: Theorizing the transformation of journalism

Journalism has enjoyed a rich and relatively stable history of professionalization. Scholars coming from a variety of disciplines have theorized this history, forming a consistent body of knowledge codified in national and international handbooks and canonical readers. However, recent work and analysis suggest that the supposed core of journalism and the assumed consistency of the inner workings of news organizations are problematic starting points for journalism studies. In this article, we challenge the consensual (self-)presentation of journalism - in terms of its occupational ideology, its professional culture, and its sedimentation in routines and organizational structures (cf. the newsroom) in the context of its reconfiguration as a post-industrial, entrepreneurial, and atypical way of working and of being at work. We outline a way beyond individualist or institutional approaches to do justice to the current complex transformation of the profession. We propose a framework to bring together these approaches in a dialectic attempt to move through and beyond journalism as it has traditionally been conceptualized and practiced, allowing for a broader definition and understanding of the myriad of practices that make up journalism.
Deep One-Class Classification

Despite the great advances made by deep learning in many machine learning problems, there is a relative dearth of deep learning approaches for anomaly detection. Those approaches which do exist involve networks trained to perform a task other than anomaly detection, namely generative models or compression, which are in turn adapted for use in anomaly detection; they are not trained on an anomaly detection based objective. In this paper we introduce a new anomaly detection method—Deep Support Vector Data Description—, which is trained on an anomaly detection based objective. The adaptation to the deep regime necessitates that our neural network and training procedure satisfy certain properties, which we demonstrate theoretically. We show the effectiveness of our method on MNIST and CIFAR-10 image benchmark datasets as well as on the detection of adversarial examples of GTSRB stop signs.
Risk Management in Construction Project

Risks have a significant impact on a construction project’s performance in terms of cost, time and quality. As the size and complexity of the projects have increased, an ability to manage risks throughout the construction process has become a central element preventing unwanted consequences. How risks are shared between the project actors is to a large extent governed by the procurement option and the content of the related contract documents. Therefore, selecting an appropriate project procurement option is a key issue for project actors. The overall aim of this research is to increase the understanding of risk management in the different procurement options: design-bid-build contracts, designbuild contracts and collaborative form of partnering. Deeper understanding is expected to contribute to a more effective risk management and, therefore, a better project output and better value for both clients and contractors. The study involves nine construction projects recently performed in Sweden and comprises a questionnaire survey and a series of interviews with clients, contractors and consultants involved in these construction projects. The findings of this work show a lack of an iterative approach to risk management, which is a weakness in current procurement practices. This aspect must be addressed if the risk management process is to serve projects and, thus, their clients. The absence of systematic risk management is especially noted in the programme phase, where it arguably has the greatest potential impact. The production phase is where most interest and activity are to be found. As a matter of practice, the communication of risks between the actors simply does not work to the extent that it must if projects are to be delivered with certainty, irrespective of the form of procurement. A clear connection between the procurement option and risk management in construction projects has been found. Traditional design-bid-build contracts do not create opportunities for open discussion of project risks and joint risk management. A number of drivers of and obstacles to effective risk management have been explored in the study. Every actor’s involvement in dialogue, effective communication and information exchange, open attitudes and trustful relationship are the factors that support open discussion of project risks and, therefore, contribute to successful risk management. Based on the findings, a number of recommendations facilitating more effective risk management have been developed for the industry practitioners. Risk Management, Risk Allocation, Construction Project, Construction Contract, Design-BidBuild, Design-Build, Partnering
Computer Aided Modeling and Optimization of Crankshaft

The Computer aided modeling and optimization analysis of crankshaft is to study was to evaluate and compare the fatigue performance of two competing manufacturing technologies for automotive crankshafts, namely forged steel and ductile cast iron. In this study a dynamic simulation was conducted on two crankshafts, cast iron and forged steel, from similar single cylinder four stroke engines.Finite element analyses was performed to obtain the variation of stress magnitude at critical locations. The dynamic analysis was done analytically and was verified by simulations in ANSYS.Results achived from aforementioned analysis were used in optimization of the forged steel crankshaft.Geometry,material and manufacturing processes were optimized considering different constraints,manufacturing feasibility and cost.The optimization process included geometry changes compatible with the current engine,fillet rolling and result in increased fatigue strength and reduced cost of the crankshaft, without changing connecting rod and engine block.
Everything You Wanted to Know about Deep Learning for Computer Vision but Were Afraid to Ask

Deep Learning methods are currently the state-of-the-art in many Computer Vision and Image Processing problems, in particular image classification. After years of intensive investigation, a few models matured and became important tools, including Convolutional Neural Networks (CNNs), Siamese and Triplet Networks, Auto-Encoders (AEs) and Generative Adversarial Networks (GANs). The field is fast-paced and there is a lot of terminologies to catch up for those who want to adventure in Deep Learning waters. This paper has the objective to introduce the most fundamental concepts of Deep Learning for Computer Vision in particular CNNs, AEs and GANs, including architectures, inner workings and optimization. We offer an updated description of the theoretical and practical knowledge of working with those models. After that, we describe Siamese and Triplet Networks, not often covered in tutorial papers, as well as review the literature on recent and exciting topics such as visual stylization, pixel-wise prediction and video processing. Finally, we discuss the limitations of Deep Learning for Computer Vision.
Security Enhanced (SE) Android: Bringing Flexible MAC to Android

The Android software stack for mobile devices defines and enforces its own security model for apps through its application-layer permissions model. However, at its foundation, Android relies upon the Linux kernel to protect the system from malicious or flawed apps and to isolate apps from one another. At present, Android leverages Linux discretionary access control (DAC) to enforce these guarantees, despite the known shortcomings of DAC. In this paper, we motivate and describe our work to bring flexible mandatory access control (MAC) to Android by enabling the effective use of Security Enhanced Linux (SELinux) for kernel-level MAC and by developing a set of middleware MAC extensions to the Android permissions model. We then demonstrate the benefits of our security enhancements for Android through a detailed analysis of how they mitigate a number of previously published exploits and vulnerabilities for Android. Finally, we evaluate the overheads imposed by our security enhancements.