nils-herrmann/clean_scirepeval huggingface dataset:
Regularization for Deep Learning: A Taxonomy

Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other. In our work we present a systematic, unifying taxonomy to categorize existing methods. We distinguish methods that affect data, network architectures, error terms, regularization terms, and optimization procedures. We do not provide all details about the listed methods; instead, we present an overview of how the methods can be sorted into meaningful categories and sub-categories. This helps revealing links and fundamental similarities between them. Finally, we include practical recommendations both for users and for developers of new regularization methods.
Assessing dominance hierarchies: validation and advantages of progressive evaluation with Elo-rating

a Junior Research Group of Primate Sexual Selection, Department of Reproductive Biology, German Primate Center Courant Research Center ‘Evolution of Social Behavior’, Georg-August-Universität, Germany c Junior Research Group of Primate Kin Selection, Department of Primatology, Max-Planck-Institute for Evolutionary Anthropology, Germany d Institute of Biology, Faculty of Bioscience, Pharmacy and Psychology, University of Leipzig, Germany e Faculty of Veterinary Medicine, Bogor Agricultural University, Indonesia
An intelligent decision support system for manufacturing technology investments

Making strategic decision on new manufacturing technology investments is difficult. New technologies are usually costly, affected by numerous factors, and the potential benefits are often hard to justify prior to implementation. Traditionally, decisions are made based upon intuition and past experience, sometimes with the support of multicriteria decision support tools. However, these approaches do not retain and reuse knowledge, thus managers are not able to make effective use of their knowledge and experience of previously completed projects to help with the prioritisation of future projects. In this paper, a hybrid intelligent system integrating case-based reasoning (CBR) and the fuzzy ARTMAP (FAM) neural network model is proposed to support managers in making timely and optimal manufacturing technology investment decisions. The system comprises a case library that holds the details of past technology investment projects. Each project proposal is characterised by a set of features determined by human experts. The FAM network is then employed to match the features of a new proposal with those from historical cases. Similar cases are retrieved and adapted, and information on these cases can be utilised as an input to prioritisation of new projects. A case study is conducted to illustrate the applicability and effectiveness of the approach, with the results presented and analysed. Implications of the proposed approach are discussed, and suggestions for further work are outlined. r 2005
Co-evolution of three megatrends nurtures uncaptured GDP – Uber ' s ridesharing revolution

Uber used a disruptive business model driven by dig ital technology to trigger a ride-sharing revolution. The institutional sources of the compa ny’s platform ecosystem architecture were analyzed to explain this revolutionary change. Both an empirical analysis of a co-existing develop ment trajectory with taxis and institutional enablers that helped to create Uber’s platform ecos ystem were analyzed. The analysis identified a correspondence with the “ wo-faced” nature of ICT that nurtures uncaptured GDP. This two-faced nature of ICT can be attributed to a virtuous cycle of decline in prices and an increase in the number of trips. We show that this cycle can be attributed to a self -propagating function that plays a vital role in the spinoff from traditional co-evolution to new co-evolution. Furthermore, we use the three mega-trends of ICT advancement, paradigm chan ge d a shift in people’s preferences to explain the secret of Uber’s system success. All these noteworthy elements seem essential to a w ell-functioning platform ecosystem architecture, not only in transportation but also f or other business institutions.
A Meta-Model for Software Development Resource Expenditures

One of the basic goals of software engineering is the establishment of useful models and equations to predict the cost of any given programming project. Many models have been proposed over the last several years, but, because of differences in the data collected, types of projects and environmental factors among software development sites, these models are not transportable and are only valid within the organization where they were developed. This result seems reasonable when one considers that a model developed at a certain environment will only be able to capture the impact of the factors which have a variable effect within that environment. Those factors which are constant at that environment, and therefore do not cause variations in the productivity among projects produced there, may have different or variable effects at another environment.
 This paper presents a model-generation process which permits the development of a resource estimation model for any particular organization. The model is based on data collected by that organization which captures its particular environmental factors and the differences among its particular projects. The process provides the capability of producing a model tailored to the organization which can be expected to be more effective than any model originally developed for another environment. It is demonstrated here using data collected from the Software Engineering Laboratory at the NASA/Goddard Space Flight Center.
The Future of Emotion Regulation Research: Capturing Context.

Emotion regulation has been conceptualized as a process by which individuals modify their emotional experiences, expressions, and physiology and the situations eliciting such emotions in order to produce appropriate responses to the ever-changing demands posed by the environment. Thus, context plays a central role in emotion regulation. This is particularly relevant to the work on emotion regulation in psychopathology, because psychological disorders are characterized by rigid responses to the environment. However, this recognition of the importance of context has appeared primarily in the theoretical realm, with the empirical work lagging behind. In this review, the author proposes an approach to systematically evaluate the contextual factors shaping emotion regulation. Such an approach consists of specifying the components that characterize emotion regulation and then systematically evaluating deviations within each of these components and their underlying dimensions. Initial guidelines for how to combine such dimensions and components in order to capture substantial and meaningful contextual influences are presented. This approach is offered to inspire theoretical and empirical work that it is hoped will result in the development of a more nuanced and sophisticated understanding of the relationship between context and emotion regulation.
Lipid-based Formulations for Danazol Containing a Digestible Surfactant, Labrafil M2125CS: In Vivo Bioavailability and Dynamic In Vitro Lipolysis

To evaluate the use of Labrafil® M2125CS as a lipid vehicle for danazol. Further, the possibility of predicting the in vivo behavior with a dynamic in vitro lipolysis model was evaluated. Danazol (28 mg/kg) was administered orally to rats in four formulations: an aqueous suspension, two suspensions in Labrafil® M2125CS (1 and 2 ml/kg) and a solution in Labrafil® M2125CS (4 ml/kg). The obtained absolute bioavailabilities of danazol were 1.5 ± 0.8%; 7.1 ± 0.6%; 13.6 ± 1.4% and 13.3 ± 3.4% for the aqueous suspension, 1, 2 and 4 ml Labrafil® M2125CS per kg respectively. Thus administration of danazol with Labrafil® M2125CS resulted in up to a ninefold increase in the bioavailability, and the bioavailability was dependent on the Labrafil® M2125CS dose. In vitro lipolysis of the formulations was able to predict the rank order of the bioavailability from the formulations, but not the absorption profile of the in vivo study. The bioavailability of danazol increased when Labrafil® M2125CS was used as a vehicle, both when danazol was suspended and solubilized in the vehicle. The dynamic in vitro lipolysis model could be used to rank the bioavailabilities of the in vivo data.
Official digital currency

In ancient times goods and services were exchanged through barter system1 Gold, valuable metals and other tangibles like stones and shells were also exploited as medium of exchange. Now Paper Currency (PC) is country-wide accepted common medium of trade. It has three major flaws. First, the holder of currency is always at risk due to theft and robbery culture in most of the societies of world. Second, counterfeit 2 currency is a challenge for currency issuing authorities. Third, printing and transferring PC causes a heavy cost. Different organizations have introduced and implemented digital currency systems but none of them is governed by any government. In this paper we introduce Official digital currency System (ODCS). Our proposed digital currency is issued and controlled by the state/central bank of a country that is why we name it Official digital currency (ODC). The process of issuing ODC is almost same as that of Conventional Paper Currency (CPC) but controlling system is different. The proposal also explains country-wide process of day to day transactions in trade through ODCS. ODC is more secure, reliable, economical and easy to use. Here we introduce just the idea and compulsory modules of ODC system and not the implementable framework. We will present the implementable framework in a separate forthcoming publication.
Guided Locality Preserving Feature Matching for Remote Sensing Image Registration

Feature matching, which refers to establishing reliable correspondences between two sets of feature points, is a critical prerequisite in feature-based image registration. This paper proposes a simple yet surprisingly effective approach, termed as guided locality preserving matching, for robust feature matching of remote sensing images. The key idea of our approach is merely to preserve the neighborhood structures of potential true matches between two images. We formulate it into a mathematical model, and derive a simple closed-form solution with linearithmic time and linear space complexities. This enables our method to accomplish the mismatch removal from thousands of putative correspondences in only a few milliseconds. To handle extremely large proportions of outliers, we further design a guided matching strategy based on the proposed method, using the matching result on a small putative set with a high inlier ratio to guide the matching on a large putative set. This strategy can also significantly boost the true matches without sacrifice in accuracy. Experiments on various real remote sensing image pairs demonstrate the generality of our method for handling both rigid and nonrigid image deformations, and it is more than two orders of magnitude faster than the state-of-the-art methods with better accuracy, making it practical for real-time applications.
On the Control of Jump Linear Markov Systems with Markov State Estimation

We analyze a jump linear Markov system being stabilized using a linear controller. We consider the case when the Markov state is associated with the probability distribution of a measured variable. We assume that the Markov state is not known, but rather is being estimated based on the observations of the variable. We present conditions for the stability of such a system and also solve the optimal LQR control problem for the case when the state estimate update uses only the last observation value. In particular we consider a suboptimal version of the causal Viterbi estimation algorithm and show that a separation property does not hold between the optimal control and the Markov state estimate. Some simple examples are also presented.
Normalized Difference Vegetation Index ( NDVI ) and Normalized Difference Water Index

Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were compared for monitoring live fuel moisture in a shrubland ecosystem. Both indices were calculated from 500 m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data covering a 33 month period from 2000 to 2002. Both NDVI and NDWI were positively correlated with live fuel moisture measured by the Los Angeles County Fire Department (LACFD). NDVI had R values ranging between 0.25 to 0.60, while NDWI had significantly higher R values, varying between 0.39 and 0.80. Water absorption measures, such as NDWI, may prove more appropriate for monitoring live fuel moisture than measures of chlorophyll absorption such as NDV
Near field focusing circular microstrip antenna array for RFID applications

A circular microstrip array with beam focused for RFID applications was presented. An analogy with the optical lens and optical diffraction was made to describe the behaviour of this system. The circular configuration of the array requires less phase shift and exhibite smaller side lobe level compare to a square array. The measurement result shows a good agreement with simulation and theory. This system is a good way to increase the efficiency of RFID communication without useless power. This solution could also be used to develop RFID devices for the localization problematic. The next step of this work is to design a system with an adjustable focus length.
Dual-polarized broad-band microstrip antennas fed by proximity coupling

This work presents a novel broad-band dual-polarized microstrip patch antenna, which is fed by proximity coupling. The microstrip line with slotted ground plane is used at two ports to feed the patch antenna. By using only one patch, the prototype antenna yields a bandwidth of 22% and 21.3% at the input port 1 and 2, respectively. The isolation between two input ports is below -34 dB across the bandwidth. Good broadside radiation patterns are observed, and the cross-polar levels are below -21 dB at both E and H planes. Due to its simple structure, it is easy to form arrays by using this antenna as an element.
A vacuum-tube guitar amplifier model using a recurrent neural network

Rock and blues guitar players prefer the use of vacuum-tube amplifiers due to the harmonic structures developed when the amplifiers are overdriven. The disadvantages of vacuum tubes compared against solid-state implementations, such as power consumption, reliability, cost, etc., are far outweighed by the desirable sound characteristics of the overdriven vacuum-tube amplifier. There are many approaches to modeling vacuum-tube amplifier behaviors in solid-state implementations. These include a variety of both analog and digital techniques, some of which are judged to be good approximations to the tube sound. In this paper we present early results of experiments in using a neural network to model the distortion produced by an overdriven vacuum-tube amplifier. Our approach is to use artificial neural networks of the recurrent variety, specifically a Nonlinear AutoRegressive eXogenous (NARX) network, to capture the nonlinear, dynamic characteristics of vacuum-tube amplifiers. NARX networks of various sizes have been trained on data sets consisting of samples of both sinusoidal and raw electric guitar signals and the amplified output of those signals applied to a tube-based amplifier driven at various levels of saturation. Models are evaluated using both quantitative (e.g., RMS error) and qualitative (listening tests) assessment methods on data sets that were not used in the network training. Listening tests- considered by us to be the most important evaluation method-at this point in the work, are indicative of the potential for success in the modeling of a vacuum-tube amplifier using a recurrent neural network.
A Study of Various Approaches and Tools on Ontology

The increasing volume and unstructured nature of data available on the World Wide Web (WWW) makes information retrieval a tedious and mechanical task. Lots of this information is not semantic driven, and hence not machine process able, but its only in human readable form. The WWW is designed to builds up a source of reference for web of meaning. Ontology information on different subjects spread globally is made available at one place. The Semantic Web (SW), moreover as an extension of WWW is designed to build as a foundation of vocabularies and effective communication of Semantics. The promising area of Semantic Web is logical and lexical semantics. Ontology plays a major role to represent information more meaningfully for humans and machines for its later effective retrieval. This paper constitutes the requisite with a unique approach for a representation and reasoning with ontology for semantic analysis of various type of document and also surveys multiple approaches for ontology learning that enables reasoning with uncertain, incomplete and contradictory information in a domain context.
Toward Scalable Social Alt Text: Conversational Crowdsourcing as a Tool for Refining Vision-to-Language Technology for the Blind

The access of visually impaired users to imagery in social media is constrained by the availability of suitable alt text. It is unknown how imperfections in emerging tools for automatic caption generation may help or hinder blind users’ understanding of social media posts with embedded imagery. In this paper, we study how crowdsourcing can be used both for evaluating the value provided by existing automated approaches and for enabling workflows that provide scalable and useful alt text to blind users. Using real-time crowdsourcing, we designed experiences that varied the depth of interaction of the crowd in assisting visually impaired users at caption interpretation, and measured trade-offs in effectiveness, scalability, and reusability. We show that the shortcomings of existing AI image captioning systems frequently hinder a user’s understanding of an image they cannot see to a degree that even clarifying conversations with sighted assistants cannot correct. Our detailed analysis of the set of clarifying conversations collected from our studies led to the design of experiences that can effectively assist users in a scalable way without the need for real-time interaction. They also provide lessons and guidelines that human captioners and the designers of future iterations of AI captioning systems can use to improve labeling of social media imagery for blind users.
Effect of optimizing Java deployment artifacts on AWS Lambda

AWS Lambda (Amazon Web Services) is the most popular serverless architecture provided by Amazon. It currently supports three platforms: JavaScript, Python, and Java Virtual Machine (JVM). The JVM could be the most complicate platform among the three as there are many languages that target the JVM platform besides Java. In addition, the complex hierarchy of dependencies, versioning, and the class loader are major issues that could cause conflict in a project. Deployment in the context of a serverless architecture means deployment as a function that represents a single service rather than as an application that is comprised of many services. AWS Lambda requires a deployment artifact to be self-contained which means all resources and dependencies must be packaged into a single jar file, and this file could be larger than AWS Lambda's allowable limit. Developers usually use build tool plugins to make self-contained artifacts, and those tools are generally unaware of what class and resource files a function needs. As a result, the artifact is not optimized. This paper demonstrates that optimization of an artifact can in general improve its resource usage and runtime performance. This paper also reports the result of an anecdotal experiment regarding the overhead of calling functions remotely in order to support design decisions in the development of AWS Lambda.
Effect of Implementation of Intermittent Straight Catheter Protocol on Rate of Urinary Tract Infections in a Trauma Population.

Catheter-associated urinary tract infections (UTIs) are a significant negative outcome. There are previous studies showing advantages in removing Foleys early but no studies of the effect of using intermittent as opposed to Foley catheterization in a trauma population. This study evaluates the effectiveness of a straight catheter protocol implemented in February 2015. A retrospective chart review was performed on all patients admitted to the trauma service at a single institution who had a UTI one year before and one year after protocol implementation on February 18, 2015. The protocol involved removing Foley catheters early and using straight catheterization. Rates were compared with Fisher's exact test and continuous data were compared using student's t test. There were 1477 patients admitted to the trauma service in the control year and 1707 in the study year. The control year had a total of 43 patients with a UTI, 28 of these met inclusion criteria. The intervention year had a total of 35 patients with a UTI and 17 met inclusion criteria. The rate of patients having a UTI went from 0.019 to 0.010 (p = 0.035). In females this rate went from 0.033 to 0.009 (p = 0.007), whereas in males it went from 0.012 to 0.010 (p = 0.837). This study shows a statistically significant improvement in the rate of UTIs after implementing an intermittent catheterization protocol suggesting that this protocol could improve the rate of UTIs in other trauma centers. We use this for all trauma patients, and it is being looked at for use hospital-wide.
Spoof surface plasmon polaritons (SSPP) based multi-band bandpass filter

This paper reports the spoof surface plasmons polaritons (SSPPs) based multi-band bandpass filter. An efficient back to back transition from Quasi TEM mode of microstrip line to SSPP mode has been designed by etching a gradient corrugated structure on the metal strip; while keeping ground plane unaltered. SSPP wave is found to be highly confined within the teeth part of corrugation. Complementary split ring resonator has been etched in the ground plane to obtained multiband bandpass filter response. Excellent conversion from QTEM mode to SSPP mode has been observed.
Essential social attributes and Habit in fitness mobile applications usage to motivate a physical activity

This paper is presented the study of the social influence by using social features in fitness mobile applications and habit that persuades the working-aged people, in the context of continuous fitness mobile application usage to promote the physical activity. Our conceptual model consisted of Habit and Social Influence. The social features based on the Persuasive Technology (1) Normative Influence, (2) Social Comparison, (3) Competition, (4) Co-operation, and (5) Social Recognition were embedded in the Social Influence construct of UTAUT2 model. The questionnaires were an instrument for this study. The target group was 443 working-aged people who live in Thailand's central region. The results reveal that the factors significantly affecting Behavioral Intention toward Use Behavior are Normative Influence, Social Comparison, Competition, and Co-operation. Only the Social Recognition is insignificantly affecting Behavioral Intention to use fitness mobile applications. The Behavioral Intention and Habit also significantly support the Use Behavior. The social features in fitness mobile application should be developed to promote the physical activity.