nils-herrmann/clean_scirepeval huggingface dataset:
Random forests for classification in ecology.

Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature.
Studies on surface wettability of poly(dimethyl) siloxane (PDMS) and glass under oxygen-plasma treatment and correlation with bond strength

An issue in microfabrication of the fluidic channels in glass/poly (dimethyl siloxane) (PDMS) is the absence of a well-defined study of the bonding strength between the surfaces making up these channels. Although most of the research papers mention the use of oxygen plasma for developing chemical (siloxane) bonds between the participating surfaces, yet they only define a certain set of parameters, tailored to a specific setup. An important requirement of all the microfluidics/biosensors industry is the development of a general regime, which defines a systematic method of gauging the bond strength between the participating surfaces in advance by correlation to a common parameter. This enhances the reliability of the devices and also gives a structured approach to its future large-scale manufacturing. In this paper, we explore the possibility of the existence of a common scale, which can be used to gauge bond strength between various surfaces. We find that the changes in wettability of surfaces owing to various levels of plasma exposure can be a useful parameter to gauge the bond strength. We obtained a good correlation between contact angle of deionized water (a direct measure of wettability) on the PDMS and glass surfaces based on various dosages or oxygen plasma treatment. The exposure was done first in an inductively coupled high-density (ICP) plasma system and then in plasma enhanced chemical vapor deposition (PECVD) system. This was followed by the measurement of bond strength by use or the standardized blister test.
Deep Image Prior

Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic image priors from a large number of example images. In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning. In order to do so, we show that a randomly-initialized neural network can be used as a handcrafted prior with excellent results in standard inverse problems such as denoising, superresolution, and inpainting. Furthermore, the same prior can be used to invert deep neural representations to diagnose them, and to restore images based on flash-no flash input pairs. Apart from its diverse applications, our approach highlights the inductive bias captured by standard generator network architectures. It also bridges the gap between two very popular families of image restoration methods: learning-based methods using deep convolutional networks and learning-free methods based on handcrafted image priors such as self-similarity.
STOCK MARKET PREDICTION AND FORECASTING TECHNIQUES : A SURVEY Mr .

This paper surveys recent literature in the area of Neural Network, Data Mining, Hidden Markov Model and Neuro-Fuzzy system used to predict the stock market fluctuation. Neural Networks and Neuro-Fuzzy systems are identified to be the leading machine learning techniques in stock market index prediction area. The Traditional techniques are not cover all the possible relation of the stock price fluctuations. There are new approaches to known in-depth of an analysis of stock price variations. NN and Markov Model can be used exclusively in the finance markets and forecasting of stock price. In this paper, we propose a forecasting method to provide better an accuracy rather traditional method. Forecasting stock return is an important financial subject that has attracted researchers’ attention for many years. It involves an assumption that fundamental information publicly available in the past has some predictive relationships to the future stock returns.
Towards Scalable Cluster Auditing through Grammatical Inference over Provenance Graphs

Investigating the nature of system intrusions in large distributed systems remains a notoriously difficult challenge. While monitoring tools (e.g., Firewalls, IDS) provide preliminary alerts through easy-to-use administrative interfaces, attack reconstruction still requires that administrators sift through gigabytes of system audit logs stored locally on hundreds of machines. At present, two fundamental obstacles prevent synergy between system-layer auditing and modern cluster monitoring tools: 1) the sheer volume of audit data generated in a data center is prohibitively costly to transmit to a central node, and 2) systemlayer auditing poses a “needle-in-a-haystack” problem, such that hundreds of employee hours may be required to diagnose a single intrusion. This paper presents Winnower, a scalable system for auditbased cluster monitoring that addresses these challenges. Our key insight is that, for tasks that are replicated across nodes in a distributed application, a model can be defined over audit logs to succinctly summarize the behavior of many nodes, thus eliminating the need to transmit redundant audit records to a central monitoring node. Specifically, Winnower parses audit records into provenance graphs that describe the actions of individual nodes, then performs grammatical inference over individual graphs using a novel adaptation of Deterministic Finite Automata (DFA) Learning to produce a behavioral model of many nodes at once. This provenance model can be efficiently transmitted to a central node and used to identify anomalous events in the cluster. We have implemented Winnower for Docker Swarm container clusters and evaluate our system against real-world applications and attacks. We show that Winnower dramatically reduces storage and network overhead associated with aggregating system audit logs, by as much as 98%, without sacrificing the important information needed for attack investigation. Winnower thus represents a significant step forward for security monitoring in distributed systems.
Using Natural Language Generation in Automatic Route Description

In this paper we tackle the problem of generating natural route descriptions on the basis of input obtained from a commercially available way-finding system. Our framework and architecture incorporates the use of general principles drawn from the domain of natural language generation. Through examples we demonstrate that it is possible to bridge the gap between underlying data representations and natural sounding linguistic descriptions. The work presented contributes both to the area of natural language generation and to the improvement of way-finding system interfaces.
A new chapter in pharmaceutical manufacturing: 3D-printed drug products.

FDA recently approved a 3D-printed drug product in August 2015, which is indicative of a new chapter for pharmaceutical manufacturing. This review article summarizes progress with 3D printed drug products and discusses process development for solid oral dosage forms. 3D printing is a layer-by-layer process capable of producing 3D drug products from digital designs. Traditional pharmaceutical processes, such as tablet compression, have been used for decades with established regulatory pathways. These processes are well understood, but antiquated in terms of process capability and manufacturing flexibility. 3D printing, as a platform technology, has competitive advantages for complex products, personalized products, and products made on-demand. These advantages create opportunities for improving the safety, efficacy, and accessibility of medicines. Although 3D printing differs from traditional manufacturing processes for solid oral dosage forms, risk-based process development is feasible. This review highlights how product and process understanding can facilitate the development of a control strategy for different 3D printing methods. Overall, the authors believe that the recent approval of a 3D printed drug product will stimulate continual innovation in pharmaceutical manufacturing technology. FDA encourages the development of advanced manufacturing technologies, including 3D-printing, using science- and risk-based approaches.
There's nothing more practical than a good theory: integrating motivational interviewing and self-determination theory.

In this article we compare and integrate two well-established approaches to motivating therapeutic change, namely self-determination theory (SDT; Deci & Ryan, 1985, ) and motivational interviewing (MI; Miller & Rollnick, 1991, ). We show that SDT's theoretical focus on the internalization of therapeutic change and on the issue of need-satisfaction is fully compatible with key principles and clinical strategies within MI. We further suggest that basic need-satisfaction might be an important mechanism accounting for the positive effects of MI. Conversely, MI principles may provide SDT researchers with new insight into the application of SDT's theoretical concept of autonomy-support, and suggest new ways of testing and developing SDT. In short, the applied approach of MI and the theoretical approach of SDT might be fruitfully married, to the benefit of both.
Models of biological pattern formation: from elementary steps to the organization of embryonic axes.

An inroad into an understanding of the complex molecular interactions on which development is based can be achieved by uncovering the minimum requirements that describe elementary steps and their linkage. Organizing regions and other signaling centers can be generated by reactions that involve local self-enhancement coupled to antagonistic reactions of longer range. More complex patterns result from a chaining of such reactions in which one pattern generates the prerequisites for the next. Patterning along the single axis of radial symmetric animals including the small freshwater polyp hydra can be explained in this way. The body pattern of such ancestral organisms evolved into the brain of higher organisms, while trunk and midline formation are later evolutionary additions. The equivalent of the hydra organizer is the blastopore, for instance, the marginal zone in amphibians. It organizes the anteroposterior axis. The Spemann organizer, located on this primary organizer, initiates and elongates the midline, which is responsible for the dorsoventral pattern. In contrast, midline formation in insects is achieved by an inhibitory signal from a dorsal organizer that restricts the midline to the ventral side. Thus, different modes of midline formation are proposed to be the points of no return in the separation of phyla. The conversion of the transient patterns of morphogenetic signaling into patterns of stable gene activation can be achieved by genes whose gene products have a positive feedback on the activity of their own gene. If several such autoregulatory genes mutually exclude each other, a cell has to make an unequivocal decision to take a particular pathway. Under the influence of a gradient, sharply confined regions with particular determinations can emerge. Borders between regions of different gene activities, and the areas of intersection of two such borders, become the new signaling centers that initiate secondary embryonic fields. As required for leg and wing formation, these new fields emerge in pairs at defined positions, with defined orientation and left-right handedness. Recent molecular-genetic results provide strong support for theoretically predicted interactions. By computer simulations it is shown that the regulatory properties of these models correspond closely to experimental observations (animated simulations are available at www.eb.tuebingen.mpg.de/meinhardt).
Design-oriented human-computer interaction

We argue that HCI has emerged as a design-oriented field of research, directed at large towards innovation, design, and construction of new kinds of information and interaction technology. But the understanding of such an attitude to research in terms of philosophical, theoretical, and methodological underpinnings seems however relatively poor within the field. This paper intends to specifically address what design 'is' and how it is related to HCI. First, three candidate accounts from design theory of what design 'is' are introduced; the conservative, the romantic, and the pragmatic. By examining the role of sketching in design, it is found that the designer becomes involved in a necessary dialogue, from which the design problem and its solution are worked out simultaneously as a closely coupled pair. In conclusion, it is proposed that we need to acknowledge, first, the role of design in HCI conduct, and second, the difference between the knowledge-generating Design-oriented Research and the artifact-generating conduct of Research-oriented Design.
2 The importance of teacher – student relationships , as explored through the lens of the NSW Quality Teaching Model

This paper aims to address the importance of supportive teacher–student interactions within the learning environment. This will be explored through the three elements of the NSW Quality Teaching Model; Intellectual Quality, Quality Learning Environment and Significance. The paper will further observe the influences of gender on the teacher–student relationship, as well as the impact that this relationship has on student academic outcomes and behaviour. Teacher–student relationships have been found to have immeasurable effects on students’ learning and their schooling experience. This paper examines the ways in which educators should plan to improve their interactions with students, in order to allow for quality learning.
A survey of techniques for internet traffic classification using machine learning

The research community has begun looking for IP traffic classification techniques that do not rely on `well known¿ TCP or UDP port numbers, or interpreting the contents of packet payloads. New work is emerging on the use of statistical traffic characteristics to assist in the identification and classification process. This survey paper looks at emerging research into the application of Machine Learning (ML) techniques to IP traffic classification - an inter-disciplinary blend of IP networking and data mining techniques. We provide context and motivation for the application of ML techniques to IP traffic classification, and review 18 significant works that cover the dominant period from 2004 to early 2007. These works are categorized and reviewed according to their choice of ML strategies and primary contributions to the literature. We also discuss a number of key requirements for the employment of ML-based traffic classifiers in operational IP networks, and qualitatively critique the extent to which the reviewed works meet these requirements. Open issues and challenges in the field are also discussed.
Argumentation Mining: State of the Art and Emerging Trends

Argumentation mining aims at automatically extracting structured arguments from unstructured textual documents. It has recently become a hot topic also due to its potential in processing information originating from the Web, and in particular from social media, in innovative ways. Recent advances in machine learning methods promise to enable breakthrough applications to social and economic sciences, policy making, and information technology: something that only a few years ago was unthinkable. In this survey article, we introduce argumentation models and methods, review existing systems and applications, and discuss challenges and perspectives of this exciting new research area.
The Tactile Internet: Applications and Challenges

Wireless communications today enables us to connect devices and people for an unprecedented exchange of multimedia and data content. The data rates of wireless communications continue to increase, mainly driven by innovation in electronics. Once the latency of communication systems becomes low enough to enable a round-trip delay from terminals through the network back to terminals of approximately 1 ms, an overlooked breakthrough?human tactile to visual feedback control?will change how humans communicate around the world. Using these controls, wireless communications can be the platform for enabling the control and direction of real and virtual objects in many situations of our life. Almost no area of the economy will be left untouched, as this new technology will change health care, mobility, education, manufacturing, smart grids, and much more. The Tactile Internet will become a driver for economic growth and innovation and will help bring a new level of sophistication to societies.
Global Image Denoising

Most existing state-of-the-art image denoising algorithms are based on exploiting similarity between a relatively modest number of patches. These patch-based methods are strictly dependent on patch matching, and their performance is hamstrung by the ability to reliably find sufficiently similar patches. As the number of patches grows, a point of diminishing returns is reached where the performance improvement due to more patches is offset by the lower likelihood of finding sufficiently close matches. The net effect is that while patch-based methods, such as BM3D, are excellent overall, they are ultimately limited in how well they can do on (larger) images with increasing complexity. In this paper, we address these shortcomings by developing a paradigm for truly global filtering where each pixel is estimated from all pixels in the image. Our objectives in this paper are two-fold. First, we give a statistical analysis of our proposed global filter, based on a spectral decomposition of its corresponding operator, and we study the effect of truncation of this spectral decomposition. Second, we derive an approximation to the spectral (principal) components using the Nyström extension. Using these, we demonstrate that this global filter can be implemented efficiently by sampling a fairly small percentage of the pixels in the image. Experiments illustrate that our strategy can effectively globalize any existing denoising filters to estimate each pixel using all pixels in the image, hence improving upon the best patch-based methods.
Evaluating and Improving Penetration Testing in Web Services

Developers often rely on penetration testing tools to detect vulnerabilities in web services, although frequently without really knowing their effectiveness. In fact, the lack of information on the internal state of the tested services and the complexity and variability of the responses analyzed, limits the effectiveness of such technique, highlighting the importance of evaluating and improving existing tools. The goal of this paper is to investigate if attack signatures and interface monitoring can be an effective mean to assess and improve the performance of penetration testing tools in web services environments. In practice, attacks performed by such tools are signed and the interfaces between the target application and external resources are monitored (e.g., between services and a database server), allowing gathering additional information on existing vulnerabilities. A prototype was implemented focusing on SQL injection vulnerabilities. The experimental evaluation results clearly show that the proposed approach can be used in real scenarios.
An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks

Solving inverse problems continues to be a challenge in a wide array of applications ranging from deblurring, image inpainting, source separation etc. Most existing techniques solve such inverse problems by either explicitly or implicitly finding the inverse of the model. The former class of techniques require explicit knowledge of the measurement process which can be unrealistic, and rely on strong analytical regularizers to constrain the solution space, which often do not generalize well. The latter approaches have had remarkable success in part due to deep learning, but require a large collection of source-observation pairs, which can be prohibitively expensive. In this paper, we propose an unsupervised technique to solve inverse problems with generative adversarial networks (GANs). Using a pre-trained GAN in the space of source signals, we show that one can reliably recover solutions to under determined problems in a ‘blind’ fashion, i.e., without knowledge of the measurement process. We solve this by making successive estimates on the model and the solution in an iterative fashion. We show promising results in three challenging applications – blind source separation, image deblurring, and recovering an image from its edge map, and perform better than several baselines.
Total Quality Management , strategic orientation and organizational performance : the case of Spanish companies

This paper examines the relationship between strategy and Total Quality Management (TQM) implementation, as well as the impact of the adaptation of both to organizational performance. We have used the emphasis on cost leadership, differentiation on marketing and differentiation on innovation as strategic dimensions to develop four great strategic configurations. The degrees of implementation of the TQM elements in each of them, as well as their associations to the various types of performances have been studied. Our results significantly support the hypotheses proposed, and suggest differences in TQM implementation depending on the selected strategy. It is also noticed that companies with greater degrees of co-alignment between their strategies and TQM are those with the highest levels of performance.
From Multifunctionality to Multiple Ecosystem Services? A Conceptual Framework for Multifunctionality in Green Infrastructure Planning for Urban Areas

Green infrastructure (GI) and ecosystem services (ES) are promoted as concepts that have potential to improve environmental planning in urban areas based on a more holistic understanding of the complex interrelations and dynamics of social–ecological systems. However, the scientific discourses around both concepts still lack application-oriented frameworks that consider such a holistic perspective and are suitable to mainstream GI and ES in planning practice. This literature review explores how multifunctionality as one important principle of GI planning can be operationalized by approaches developed and tested in ES research. Specifically, approaches developed in ES research can help to assess the integrity of GI networks, balance ES supply and demand, and consider trade-offs. A conceptual framework for the assessment of multifunctionality from a social–ecological perspective is proposed that can inform the design of planning processes and support stronger exchange between GI and ES research.