nils-herrmann/clean_scirepeval huggingface dataset:
Bioarchitecture: bioinspired art and architecture--a perspective.

Art and architecture can be an obvious choice to pair with science though historically this has not always been the case. This paper is an attempt to interact across disciplines, define a new genre, bioarchitecture, and present opportunities for further research, collaboration and professional cooperation. Biomimetics, or the copying of living nature, is a field that is highly interdisciplinary, involving the understanding of biological functions, structures and principles of various objects found in nature by scientists. Biomimetics can lead to biologically inspired design, adaptation or derivation from living nature. As applied to engineering, bioinspiration is a more appropriate term, involving interpretation, rather than direct copying. Art involves the creation of discrete visual objects intended by their creators to be appreciated by others. Architecture is a design practice that makes a theoretical argument and contributes to the discourse of the discipline. Bioarchitecture is a blending of art/architecture and biomimetics/bioinspiration, and incorporates a bioinspired design from the outset in all parts of the work at all scales. Herein, we examine various attempts to date of art and architecture to incorporate bioinspired design into their practice, and provide an outlook and provocation to encourage collaboration among scientists and designers, with the aim of achieving bioarchitecture.This article is part of the themed issue 'Bioinspired hierarchically structured surfaces for green science'.
Purposeful selection of variables in logistic regression

The main problem in many model-building situations is to choose from a large set of covariates those that should be included in the "best" model. A decision to keep a variable in the model might be based on the clinical or statistical significance. There are several variable selection algorithms in existence. Those methods are mechanical and as such carry some limitations. Hosmer and Lemeshow describe a purposeful selection of covariates within which an analyst makes a variable selection decision at each step of the modeling process. In this paper we introduce an algorithm which automates that process. We conduct a simulation study to compare the performance of this algorithm with three well documented variable selection procedures in SAS PROC LOGISTIC: FORWARD, BACKWARD, and STEPWISE. We show that the advantage of this approach is when the analyst is interested in risk factor modeling and not just prediction. In addition to significant covariates, this variable selection procedure has the capability of retaining important confounding variables, resulting potentially in a slightly richer model. Application of the macro is further illustrated with the Hosmer and Lemeshow Worchester Heart Attack Study (WHAS) data. If an analyst is in need of an algorithm that will help guide the retention of significant covariates as well as confounding ones they should consider this macro as an alternative tool.
Survey on Software Vulnerability Analysis Method Based on Machine Learning

With the increasingly rich of vulnerability related data and the extensive application of machine learning methods, software vulnerability analysis methods based on machine learning is becoming an important research area of information security. In this paper, the up-to-date and well-known works in this research area were analyzed deeply. A framework for software vulnerability analysis based on machine learning was proposed. And the existing works were described and compared, the limitations of these works were discussed. The future research directions on software vulnerability analysis based on machine learning were put forward in the end.
Vehicle type classification and attribute prediction using multi-task RCNN

Vehicle classification is an important subject of study due to its significance in a number of areas including law enforcement, traffic surveillance, autonomous navigation, and transportation management. While numerous approaches have been proposed, few studies have been published with regard to the multi-view classification of vehicles captured in real surveillance. In this paper, we consider the multi-view classification of vehicles as an attribute prediction problem with views (rear, front, and side) as attributes. The corresponding multi-task learning is implemented in the Region-based Convolutional Neural Network (RCNN) framework, which classifies vehicle categories (car, truck, bus, and van) and predicts the attributes simultaneously. Experiments on a field-captured vehicle dataset provide satisfactory results, with approximate 83% accuracy for vehicle type classification and over 90% accuracy for attribute prediction.
Neurolinguistics: Structural plasticity in the bilingual brain

Humans have a unique ability to learn more than one language — a skill that is thought to be mediated by functional (rather than structural) plastic changes in the brain. Here we show that learning a second language increases the density of grey matter in the left inferior parietal cortex and that the degree of structural reorganization in this region is modulated by the proficiency attained and the age at acquisition. This relation between grey-matter density and performance may represent a general principle of brain organization.
An overview of workflow management: From process modeling to workflow automation infrastructure

Today's business enterprises must deal with global competition, reduce the cost of doing business, and rapidly develop new services and products. To address these requirements enterprises must constantly reconsider and optimize the way they do business and change their information systems and applications to support evolving business processes. Workflow technology facilitates these by providing methodologies and software to support (i) business process modeling to capture business processes as workflow specifications, (ii) business process reengineering to optimize specified processes, and (iii) workflow automation to generate workflow implementations from workflow specifications. This paper provides a high-level overview of the current workflow management methodologies and software products. In addition, we discuss the infrastructure technologies that can address the limitations of current commercial workflow technology and extend the scope and mission of workflow management systems to support increased workflow automation in complex real-world environments involving heterogeneous, autonomous, and distributed information systems. In particular, we discuss how distributed object management and customized transaction management can support further advances in the commercial state of the art in this area.
Evaluation of transaction authentication methods for online banking

Authentication is a major research topic in the information security field. Much has been written about assessing entity (user) authentication methods, but there is a lack of literature concerning the evaluation of financial transaction authentication in online banking. Entity authentication methods have been systematized by quantifying their qualitative aspects, but there is no evaluation mechanism which also places the additional characteristics of transaction authentication in a user-centric context. Based on an existing mechanism which quantifies accessibility, memorability, security and vulnerability characteristics in entity authenticationmethods, we propose feasibility as an additional dimensionwhich quantifies aspects related to the secure usability of transaction authentication methods. We also propose the use of this evaluation mechanism by multiple raters to reduce personal bias. Four implemented and eight proposed authentication methods for online banking were evaluated by seven experts. The results indicate that the mechanism can be applied on a wide range of authentication methods, since it is able to evaluate methods based on different information schemes. However, care must be taken that evaluations are performed by multiple experts, due to the amount of subjectivity inherent in the mechanism and in the different opinions of the raters.
Advances in Pre-Training Distributed Word Representations

Many Natural Language Processing applications nowadays rely on pre-trained word representations estimated from large text corpora such as news collections, Wikipedia and Web Crawl. In this paper, we show how to train high-quality word vector representations by using a combination of known tricks that are however rarely used together. The main result of our work is the new set of publicly available pre-trained models that outperform the current state of the art by a large margin on a number of tasks.
Business Models and the Internet of Things

This article provides theoretically and practically grounded assistance to companies that are today engaged primarily in non‐digital industries in the development and implementation of business models that use the Internet of Things. To that end, we investigate the role of the Internet in business models in general in the first section. We conclude that the significance of the Internet in business model innovation has increased steadily since the 1990s, that each new Internet wave has given rise to new digital business model patterns, and that the biggest breakthroughs to date have been made in digital industries. In the second section, we show that digital business model patterns have now become relevant in physical industries as well. The separation between physical and digital industries is now consigned to the past. The key to this transformation is the Internet of Things which makes possible hybrid solutions that merge physical products and digital services. From this, we derive very general business model logic for the Internet of Things and some specific components and patterns for business models. Finally we sketch out the central challenges faced in implementing such hybrid business models and point to possible solutions. The Influence of the Internet on Business Models to Date
Don't forget, there is more than forgetting: new metrics for Continual Learning

Continual learning consists of algorithms that learn from a stream of data/tasks continuously and adaptively thought time, enabling the incremental development of ever more complex knowledge and skills. The lack of consensus in evaluating continual learning algorithms and the almost exclusive focus on forgetting motivate us to propose a more comprehensive set of implementation independent metrics accounting for several factors we believe have practical implications worth considering in the deployment of real AI systems that learn continually: accuracy or performance over time, backward and forward knowledge transfer, memory overhead as well as computational efficiency. Drawing inspiration from the standard Multi-Attribute Value Theory (MAVT) we further propose to fuse these metrics into a single score for ranking purposes and we evaluate our proposal with five continual learning strategies on the iCIFAR-100 continual learning benchmark.
A computational framework for detecting offensive language with support vector machine in social communities

The use of the social media sites are growing rapidly to interact with the communities and to share the ideas among others. It may happen that most of the people dislike the ideas of others person views and make the use of the offensive language in their posts. Due to these offensive terms, many people especially youth and teenagers try to adopt such language and spread over the social media sites which may significantly affect the others people innocent minds. As offensive terms increasingly use by the people in highly manner, it is difficult to find or classify such offensive terms in real day to day life. To overcome from these problem, the proposed system analyze the offensive language and can classify the offensive sentence on a particular topic discussion using the support vector machine (SVM) as supervised classification in the data mining. The proposed system also can find the potential user by means of whom the offensive language spread among others and define the comparative analysis of SVM with Naive Bayes technique.
Design and implementation of a micro-power CMOS voltage reference circuit based on thermal compensation of Vgs

In this work a simple all MOS voltage reference circuit has been proposed. To obtain reference output voltage, the thermal compensation has been generated by using a series composite NMOSTs. The voltage reference circuit has been fabricated in a standard 0.18 μm CMOS technology. The proposed circuit is capable of working for the supply voltage ranging from 1.25 V to 2 V. The maximum power dissipation of the proposed circuit is 0.48 μWat the supply voltage of 2 V. The measurement has been performed over a set of 10 samples. It resulted in the mean temperature coefficient (TC) of 19.302 ppm/1C for the temperature range of 40 1C to 85 1C. The measured mean line sensitivity is 2.217 mV/V for the supply voltage ranging from 1.25 V to 2 V at the room temperature. The measured mean power supply rejection ratio at 10 Hz and 1 MHz is 55.31 dB and 16.67 dB respectively for the supply voltage of 1.8 V. Moreover, the measured mean noise density without any filtering capacitor at 100 Hz and 100 kHz are 12:39 μV= ffiffiffiffiffiffi Hz p and 0:39 μV= ffiffiffiffiffiffi Hz p respectively. Due to its simple circuit implementation, the active area of the circuit is 0.0077 mm. & 2014 Elsevier Ltd. All rights reserved.
Named Entity Recognition

Named entity recognition (NER) is a subtask of information extraction that seeks to locate and classify atomic elements in text into predefined categories such as the names of persons, organizations, locations, expressions of times, quantities, monetary values, percentages, etc. We use the JavaNLP repository(http://nlp.stanford.edu/javanlp/ ) for its implementation of a Conditional Random Field(CRF) and a Conditional Markov Model(CMM), also called a Maximum Entropy Markov Model. We have obtained results on majority voting with different labeling schemes, with backward and forward parsing of the CMM, and also some results when we trained a decision tree to take a decision based on the outputs of the different labeling schemes. We have also tried to solve the problem of label inconsistency issue by attempting the naive approach of enforcing hard label-consistency by choosing the majority entity for a sequence of tokens, in the specific test document, as well as the whole test corpus, and managed to get reasonable gains. We also attempted soft label consistency in the following way. We use a portion of the training data to train a CRF to make predictions on the rest of the train data and on the test data. We then train a second CRF with the majority label predictions as additional input features.
Adversarial learning in credit card fraud detection

Credit card fraud is an expensive problem for many financial institutions, costing billions of dollars to companies annually. Many adversaries still evade fraud detection systems because these systems often do not include information about the adversary's knowledge of the fraud detection mechanism. This project aims to include information about the “fraudster's” motivations and knowledge base into an adaptive fraud detection system. In this project, we use a game theoretical adversarial learning approach in order to model the fraudster's best strategy and pre-emptively adapt the fraud detection system to better classify these future fraudulent transactions. Using a logistic regression classifier as the fraud detection mechanism, we initially identify the best strategy for the adversary based on the number of fraudulent transactions that go undetected, and assume that the adversary uses this strategy for future transactions in order to improve our classifier. Prior research has used game theoretic models for adversarial learning in the domains of credit card fraud and email spam, but this project adds to the literature by extending these frameworks to a practical, real-world data set. Test results show that our adversarial framework produces an increasing AUC score on validation sets over several iterations in comparison to the static model usually employed by credit card companies.
A Survey on Mobile Edge Computing: The Communication Perspective

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized mobile cloud computing toward mobile edge computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also discuss a set of issues, challenges, and future research directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.
Multi-source data analysis for bike sharing systems

Bike sharing systems (BSSs) have become common in many cities worldwide, providing a new transportation mode for residents' commutes. However, the management of these systems gives rise to many problems. As the bike pick-up demands at different places are unbalanced at times, the systems have to be rebalanced frequently. Rebalancing the bike availability effectively, however, is very challenging as it demands accurate prediction for inventory target level determination. In this work, we propose two types of regression models using multi-source data to predict the hourly bike pick-up demand at cluster level: Similarity Weighted K-Nearest-Neighbor (SWK) based regression and Artificial Neural Network (ANN). SWK-based regression models learn the weights of several meteorological factors and/or taxi usage and use the correlation between consecutive time slots to predict the bike pick-up demand. The ANN is trained by using historical trip records of BSS, meteorological data, and taxi trip records. Our proposed methods are tested with real data from a New York City BSS: Citi Bike NYC. Performance comparison between SWK-based and ANN-based methods is provided. Experimental results indicate the high accuracy of ANN-based prediction for bike pick-up demand using multisource data.
The Resource-Based View and Information Systems Research: Review, Extension, and Suggestions for Future Research

21 22 Information systems researchers have a long tra23 dition of drawing on theories from disciplines such 24 as economics, computer science, psychology, and 25 general management and using them in their own 26 research. Because of this, the information sys27 tems field has become a rich tapestry of theore28 Jane Webster was the accepting senior editor for this 29 paper. 30 tical and conceptual foundations. As new theories are brought into the field, particularly theories that have become dominant in other areas, there may be a benefit in pausing to assess their use and contribution in an IS context. The purpose of this paper is to explore and critically evaluate use of the resource-based view of the firm (RBV) by IS researchers. The paper provides a brief review of resourcebased theory and then suggests extensions to make the RBV more useful for empirical IS research. First, a typology of key IS resources is presented, and these are then described using six traditional resource attributes. Second, we emphasize the particular importance of looking at both resource complementarity and moderating factors when studying IS resource effects on firm performance. Finally, we discuss three considerations that IS researchers need to address when using the RBV empirically. Eight sets of propositions are advanced to help guide future
Overview and Comparison of Gate Level Quantum Software Platforms

Quantum computers are available to use over the cloud, but the recent explosion of quantum software platforms can be overwhelming for those deciding on which to use. In this paper, we provide a current picture of the rapidly evolving quantum computing landscape by comparing four software platforms—Forest (pyQuil), QISKit, ProjectQ, and the Quantum Developer Kit—that enable researchers to use real and simulated quantum devices. Our analysis covers requirements and installation, language syntax through example programs, library support, and quantum simulator capabilities for each platform. For platforms that have quantum computer support, we compare hardware, quantum assembly languages, and quantum compilers. We conclude by covering features of each and briefly mentioning other quantum computing software packages.
Robust method to retrieve the constitutive effective parameters of metamaterials.

We propose an improved method to retrieve the effective constitutive parameters (permittivity and permeability) of a slab of metamaterial from the measurement of S parameters. Improvements over existing methods include the determination of the first boundary and the thickness of the effective slab, the selection of the correct sign of effective impedance, and a mathematical method to choose the correct branch of the real part of the refractive index. The sensitivity of the effective constitutive parameters to the accuracy of the S parameters is also discussed. The method has been applied to various metamaterials and the successful retrieval results prove its effectiveness and robustness.
Filtering spam e-mail from mixed arabic and english messages: a comparison of machine learning techniques

Spam is one of the main problems in emails communications. As the volume of non-english language spam increases, little work is done in this area. For example, in Arab world users receive spam written mostly in arabic, english or mixed Arabic and english. To filter this kind of messages, this research applied several machine learning techniques. Many researchers have used machine learning techniques to filter spam email messages. This study compared six supervised machine learning classifiers which are maximum entropy, decision trees, artificial neural nets, naïve bayes, support system machines and k-nearest neighbor. The experiments suggested that words in Arabic messages should be stemmed before applying classifier. In addition, in most cases, experiments showed that classifiers using feature selection techniques can achieve comparable or better performance than filters do not used them.