nils-herrmann/clean_scirepeval huggingface dataset:
MarketBusting: strategies for exceptional business growth.

If company leaders were granted a single wish, it would surely be for a reliable way to create new growth businesses. Business practitioners'overwhelming interest in this subject prompted the authors to conduct a three-year study of organizational growth--specifically, to find out which growth strategies were most successful. They discovered, somewhat to their surprise, that even companies in mature industries found rich new sources of growth when they reconfigured their unit of business (what they bill customers for) or their key metrics (how they measure success). In this article, the authors outline these and other moves companies can make to redefine their profit drivers and realize low-risk growth. They offer plenty of real-world examples. For instance: CHANGING YOUR UNIT OF BUSINESS: Once a conventional printing house, Madden Communications not only prints promotional materials for customers but also manages the distribution and installation of those materials on-site. Its revenues grew from dollars 1o million in 1990 to dollars 133 million in 2004, in an industry that many had come to regard as hopelessly mature. IMPROVING YOUR KEY METRICS-PARTICULARLY PRODUCTIVITY: Lamons Gasket, with dollars 80 million in revenues, built a Web site that radically improved its customers' ability to find, order, and pay for goods. The firm's market share rose along with its customer retention rate. The authors also suggest ways to identify your unit of business and associated key metrics and recognize the obstacles to changing them; review the key customer segments you serve; assess the need for new capabilities and the potential for internal resistance to change; and communicate to internal and external constituencies the changes you wish to make in your unit of business or key metrics.
Influence of ploidy level on morphology, growth and drought susceptibility in Spathiphyllum wallisii

In this study, we analysed morphological, anatomical and physiological effects of polyploidisation in Spathiphyllum wallisii in order to evaluate possible interesting advantages of polyploids for ornamental breeding. Stomatal density was negatively correlated with increased ploidy level. Stomatal size increased in polyploids. Tetraploid Spathiphyllum plants had more ovate and thicker leaves. The inflorescence of tetraploids had a more ovate and thicker spathum, a more cylindrical spadix and a thicker but shorter flower stalk. Biomass production of the tetraploids was reduced, as expressed by lower total dry weights, and tetraploids produced fewer shoots and leaves compared with their diploid progenitors. Furthermore, tetraploid Spathiphyllum plants were more resistant to drought stress compared with diploid plants. After 15 days of drought stress, diploids showed symptoms of wilting, while the tetraploids showed almost no symptoms. Further, measurements of stomatal resistance, leaf water potential, relative water content and proline content indicated that the tetraploid genotypes were more resistant to drought stress compared with the diploids.
Reachability Analysis and Safety Verification for Neural Network Control Systems

Autonomous cyber-physical systems (CPS) rely on the correct operation of numerous components, with state-of-the-art methods relying on machine learning (ML) and artificial intelligence (AI) components in various stages of sensing and control. This paper develops methods for estimating the reachable set and verifying safety properties of dynamical systems under control of neural networkbased controllers that may be implemented in embedded software. The neural network controllers we consider are feedforward neural networks called multilayer perceptrons (MLP) with general activation functions. As such feedforward networks are memoryless, they may be abstractly represented as mathematical functions, and the reachability analysis of the network amounts to range (image) estimation of this function provided a set of inputs. By discretizing the input set of the MLP into a finite number of hyper-rectangular cells, our approach develops a linear programming (LP) based algorithm for over-approximating the output set of the MLP with its input set as a union of hyperrectangular cells. Combining the over-approximation for the output set of an MLP based controller and reachable set computation routines for ordinary difference/differential equation (ODE) models, an algorithm is developed to estimate the reachable set of the closed-loop system. Finally, safety verification for neural network control systems can be performed by checking the existence of intersections between the estimated reachable set and unsafe regions. The approach is implemented in a computational software prototype and evaluated on numerical examples.
Variational Adversarial Deep Domain Adaptation for Health Care Time Series Analysis

Data-driven machine learning, in particular deep learning, is improving state-ofthe-art in many healthcare prediction tasks. A current standard protocol is to collect patient data to build, evaluate, and deploy machine learning algorithms for specific age groups (say source domain), which, if not properly trained, can perform poorly on data from other age groups (target domains). In this paper, we address the question of whether it is possible to adapt machine learning models built for one age group to also perform well on other age groups. Additionally, healthcare time series data is also challenging in that it is usually longitudinal and episodic with the potential of having complex temporal relationships. We address these problems with our proposed adversarially trained Variational Adversarial Deep Domain Adaptation (VADDA) model built atop a variational recurrent neural network, which has been shown to be capable of capturing complex temporal latent relationships. We assume and empirically justify that patient data from different age groups can be treated as being similar but different enough to be classified as coming from different domains, requiring the use of domain-adaptive approaches. Through experiments on the MIMIC-III dataset we demonstrate that our model outperforms current state-of-the-art domain adaptation approaches, being (as far as we know) the first to accomplish this for healthcare time-series data.
Towards SMS Spam Filtering : Results under a New Dataset

The growth of mobile phone users has lead to a dramatic increasing of SMS spam messages. Recent reports clearly indicate that the volume of mobile phone spam is dramatically increasing year by year. In practice, fighting such plague is difficult by several factors, including the lower rate of SMS that has allowed many users and service providers to ignore the issue, and the limited availability of mobile phone spam-filtering software. Probably, one of the major concerns in academic settings is the scarcity of public SMS spam datasets, that are sorely needed for validation and comparison of different classifiers. Moreover, traditional content-based filters may have their performance seriously degraded since SMS messages are fairly short and their text is generally rife with idioms and abbreviations. In this paper, we present details about a new real, public and non-encoded SMS spam collection that is the largest one as far as we know. Moreover, we offer a comprehensive analysis of such dataset in order to ensure that there are no duplicated messages coming from previously existing datasets, since it may ease the task of learning SMS spam classifiers and could compromise the evaluation of methods. Additionally, we compare the performance achieved by several established machine learning techniques. In summary, the results indicate that the procedure followed to build the collection does not lead to near-duplicates and, regarding the classifiers, the Support Vector Machines outperforms other evaluated techniques and, hence, it can be used as a good baseline for further comparison. Mobile phone spam; SMS spam; spam filtering; text categorization; classification.
Overview of 3GPP Release 14 Enhanced NB-IoT

In 3GPP LTE Release 13, Narrowband Internet of Things (NB-IoT) was standardized for providing wide-area connectivity for massive machine-type communications for IoT. In LTE Release 14, NB-IoT was further developed to deliver enhanced user experience in selected areas through the addition of features such as increased positioning accuracy, increased peak data rates, the introduction of a lower device power class, improved non-anchor carrier operation, multicast, and authorization of coverage enhancements. In this article, we provide an overview of these features introduced for NB-IoT in LTE Release 14. An analysis is given on the applicability of these features and their benefits to enhance the NB-IoT radio access technology.
Viewpoint: reflections on a well-traveled path: self-awareness, mindful practice, and relationship-centered care as foundations for medical education.

Medical students enter medical school hoping to have good relationships with their patients. Along with residents, however, they are exposed to a hidden curriculum that places the acquisition of biomedical knowledge above and at times at odds with development of the awareness and relationship skills important to the patient-physician relationship. Seasoned clinicians often enjoy the capacity for rich, healing relationships that are marked by reciprocal influence between them and their patients. The author argues that it is not necessary to relegate this recapturing of the human side of medicine to a midcareer epiphany, and the author calls for educational measures to encourage development of the communication and relationship-building skills throughout the medical education process. This will require a paradigm shift to a culture where teachers and learners are willing to consciously attend to their relationships and to work on self-awareness and mindfulness while they also master the biomedical knowledge required of the profession. Medical educators can facilitate and support continuous development of these skills throughout medical school and residency. Within the curriculum, there are many opportunities to teach how to reflect and to guide those reflections in ways that enhance our students' and residents' understanding of themselves as individuals and in the relationships they form with their patients. Using examples from narratives gathered in workshops and on work rounds with students and residents at the University of Washington School of Medicine, the author explores the concepts of relationship-centered care, self-awareness, and mindfulness as proposed cornerstones of a new foundation for medical education.
Spatially Adaptive Computation Time for Residual Networks

This paper proposes a deep learning architecture based on Residual Network that dynamically adjusts the number of executed layers for the regions of the image. This architecture is end-to-end trainable, deterministic and problem-agnostic. It is therefore applicable without any modifications to a wide range of computer vision problems such as image classification, object detection and image segmentation. We present experimental results showing that this model improves the computational efficiency of Residual Networks on the challenging ImageNet classification and COCO object detection datasets. Additionally, we evaluate the computation time maps on the visual saliency dataset cat2000 and find that they correlate surprisingly well with human eye fixation positions.
Affordance Extraction and Inference based on Semantic Role Labeling

Common-sense reasoning is becoming increasingly important for the advancement of Natural Language Processing. While word embeddings have been very successful, they cannot explain which aspects of ‘coffee’ and ‘tea’ make them similar, or how they could be related to ‘shop’. In this paper, we propose an explicit word representation that builds upon the Distributional Hypothesis to represent meaning from semantic roles, and allow inference of relations from their meshing, as supported by the affordance-based Indexical Hypothesis. We find that our model improves the state-of-the-art on unsupervised word similarity tasks while allowing for direct inference of new relations from the same vector space.
Dynamic trust management for internet of things applications

We propose a dynamic trust management protocol for Internet of Things (IoT) systems to deal with misbehaving nodes whose status or behavior may change dynamically. We consider an IoT system being deployed in a smart community where each node autonomously performs trust evaluation. We provide a formal treatment of the convergence, accuracy, and resilience properties of our dynamic trust management protocol and validate these desirable properties through simulation. We demonstrate the effectiveness of our dynamic trust management protocol with a trust-based service composition application in IoT environments. Our results indicate that trust-based service composition significantly outperforms non-trust-based service composition and approaches the maximum achievable performance based on ground truth status. Furthermore, our dynamic trust management protocol is capable of adaptively adjusting the best trust parameter setting in response to dynamically changing environments to maximize application performance.
Significance of long chain polyunsaturated fatty acids in human health

In the last decades, the development of new technologies applied to lipidomics has revitalized the analysis of lipid profile alterations and the understanding of the underlying molecular mechanisms of lipid metabolism, together with their involvement in the occurrence of human disease. Of particular interest is the study of omega-3 and omega-6 long chain polyunsaturated fatty acids (LC-PUFAs), notably EPA (eicosapentaenoic acid, 20:5n-3), DHA (docosahexaenoic acid, 22:6n-3), and ARA (arachidonic acid, 20:4n-6), and their transformation into bioactive lipid mediators. In this sense, new families of PUFA-derived lipid mediators, including resolvins derived from EPA and DHA, and protectins and maresins derived from DHA, are being increasingly investigated because of their active role in the "return to homeostasis" process and resolution of inflammation. Recent findings reviewed in the present study highlight that the omega-6 fatty acid ARA appears increased, and omega-3 EPA and DHA decreased in most cancer tissues compared to normal ones, and that increments in omega-3 LC-PUFAs consumption and an omega-6/omega-3 ratio of 2-4:1, are associated with a reduced risk of breast, prostate, colon and renal cancers. Along with their lipid-lowering properties, omega-3 LC-PUFAs also exert cardioprotective functions, such as reducing platelet aggregation and inflammation, and controlling the presence of DHA in our body, especially in our liver and brain, which is crucial for optimal brain functionality. Considering that DHA is the principal omega-3 FA in cortical gray matter, the importance of DHA intake and its derived lipid mediators have been recently reported in patients with major depressive and bipolar disorders, Alzheimer disease, Parkinson's disease, and amyotrophic lateral sclerosis. The present study reviews the relationships between major diseases occurring today in the Western world and LC-PUFAs. More specifically this review focuses on the dietary omega-3 LC-PUFAs and the omega-6/omega-3 balance, in a wide range of inflammation disorders, including autoimmune diseases. This review suggests that the current recommendations of consumption and/or supplementation of omega-3 FAs are specific to particular groups of age and physiological status, and still need more fine tuning for overall human health and well being.
Representing Documents and Queries as Sets of Word Embedded Vectors for Information Retrieval

A major difficulty in applying word vector embeddings in information retrieval is in devising an effective and efficient strategy for obtaining representations of compound units of text, such as whole documents, (in comparison to the atomic words), for the purpose of indexing and scoring documents. Instead of striving for a suitable method to obtain a single vector representation of a large document of text, we aim to develop a similarity metric that makes use of the similarities between the individual embedded word vectors in a document and a query. More specifically, we represent a document and a query as sets of word vectors, and use a standard notion of similarity measure between these sets, computed as a function of the similarities between each constituent word pair from these sets. We then make use of this similarity measure in combination with standard information retrieval based similarities for document ranking. The results of our initial experimental investigations show that our proposed method improves MAP by up to 5.77%, in comparison to standard text-based language model similarity, on the TREC 6, 7, 8 and Robust ad-hoc test collections.
Bootstrapping Generators from Noisy Data

A core step in statistical data-to-text generation concerns learning correspondences between structured data representations (e.g., facts in a database) and associated texts. In this paper we aim to bootstrap generators from large scale datasets where the data (e.g., DBPedia facts) and related texts (e.g., Wikipedia abstracts) are loosely aligned. We tackle this challenging task by introducing a special-purpose content selection mechanism.1 We use multi-instance learning to automatically discover correspondences between data and text pairs and show how these can be used to enhance the content signal while training an encoder-decoder architecture. Experimental results demonstrate that models trained with content-specific objectives improve upon a vanilla encoder-decoder which solely relies on soft attention.
Factors Affecting Customer Loyalty of Using Internet Banking in Malaysia

Internet banking (IB) has become one of the widely used banking services among Malaysian retail banking customers in recent years. Despite its attractiveness, customer loyalty towards Internet banking website has become an issue due to stiff competition among the banks in Malaysia. As the development and validation of a customer loyalty model in Internet banking website context in Malaysia had not been addressed by past studies, this study attempts to develop a model based on the usage of Information System (IS), with the purpose to investigate factors influencing customer loyalty towards Internet banking websites. A questionnaire survey was conducted with the sample consisting of Internet banking users in Malaysia. Factors that influence customer loyalty towards Internet banking website in Malaysia have been investigated and tested. The study also attempts to identify the most essential factors among those investigated: service quality, perceived value, trust, habit and reputation of the bank. Based on the findings, trust, habit and reputation are found to have a significant influence on customer loyalty towards individual Internet banking websites in Malaysia. As compared to trust or habit factors, reputation is the strongest influence. The results also indicated that service quality and perceived value are not significantly related to customer loyalty. Service quality is found to be an important factor in influencing the adoption of the technology, but did not have a significant influence in retention of customers. The findings have provided an insight to the internet banking providers on the areas to be focused on in retaining their customers.
Right Answer for the Wrong Reason: Discovery and Mitigation

Exposing the weaknesses of neural models is crucial for improving their performance and robustness in real-world applications. One common approach is to examine how input perturbations affect the output. Our analysis takes this to an extreme on natural language processing tasks by removing as many words as possible from the input without changing the model prediction. For question answering and natural language inference, this often reduces the inputs to just one or two words, while model confidence remains largely unchanged. This is an undesireable behavior: the model gets the Right Answer for the Wrong Reason (RAWR). We introduce a simple training technique that mitigates this problem while maintaining performance on regular examples.
Computer-Assisted Auditing Tools Acceptance Using I-Toe: A New Paradigm

This paper presents a new adoption framework i.e. Individual, Technology, Organization and Environment (I-TOE) to address the factors influencing computer-assisted auditing tools (CAATs) acceptance in public audit firms. CAATs are audit technology that helps in achieving effective and efficient audit work. While CAATs adoption varies among audit departments, prior studies focused narrowly on CAATs acceptance issues from the individual perspective and no comprehensive study has been done that focused on both organization and individual standpoints. Realizing this gap, this paper aims to predict CAATs adoption factors using the I-TOE framework. I-TOE stresses on the relationship of Individuals factors (i.e. performance expectancy, effort expectancy, social influence, facilitating condition, hedonic motivation and habit), CAATs Technology (i.e. technology cost-benefit, risk and technology fit), Organization characteristics (i.e. size, readiness and top management), and Environment factors (i.e. client’s AIS complexity, competitive pressure and professional accounting body regulations) towards CAATs acceptance. It integrates both Unified Theory of Acceptance and Use of Technology 2 and Technology-Organization-Environment framework. I-TOE provides a comprehensive model that helps audit firms and regulatory bodies to develop strategies and policies to increase CAATs adoption. Empirical study through questionnaire survey will be conducted to validate I-TOE model.