nils-herrmann/clean_scirepeval huggingface dataset:
When 25 Cents is Too Much: An Experiment on Willingness-To-Sell and Willingness-To-Protect Personal Information

We present an empirical study of willingness-to-pay for protecting information (we term it willingness-to-protect) and willingness-to-accept a proposal to sell information (willingness-to-accept). We conducted the study in two parts. In the first part we presented the study participants with two single closed-ended yes-no questions, asking them to protect themselves against information release for a fixed amount of money, or offering them to release their information for a fixed amount of money. In the second part we asked subjects to specify their maximum willingness-to-protect for personal data and their minimum willingness-to-accept for the release of personal information. Our results show a clear preference across the vast majority of participants for money in exchange for data in both the protection and release scenarios, even if the actual monetary advantage of selling or not protecting in the close-ended choice situations is very small. In all hypothetical scenarios, we find that the average willingness-to-accept is dramatically higher than the average willingness-to-protect.
DREAM: Diabetic Retinopathy Analysis Using Machine Learning

This paper presents a computer-aided screening system (DREAM) that analyzes fundus images with varying illumination and fields of view, and generates a severity grade for diabetic retinopathy (DR) using machine learning. Classifiers such as the Gaussian Mixture model (GMM), k-nearest neighbor (kNN), support vector machine (SVM), and AdaBoost are analyzed for classifying retinopathy lesions from nonlesions. GMM and kNN classifiers are found to be the best classifiers for bright and red lesion classification, respectively. A main contribution of this paper is the reduction in the number of features used for lesion classification by feature ranking using Adaboost where 30 top features are selected out of 78. A novel two-step hierarchical classification approach is proposed where the nonlesions or false positives are rejected in the first step. In the second step, the bright lesions are classified as hard exudates and cotton wool spots, and the red lesions are classified as hemorrhages and micro-aneurysms. This lesion classification problem deals with unbalanced datasets and SVM or combination classifiers derived from SVM using the Dempster-Shafer theory are found to incur more classification error than the GMM and kNN classifiers due to the data imbalance. The DR severity grading system is tested on 1200 images from the publicly available MESSIDOR dataset. The DREAM system achieves 100% sensitivity, 53.16% specificity, and 0.904 AUC, compared to the best reported 96% sensitivity, 51% specificity, and 0.875 AUC, for classifying images as with or without DR. The feature reduction further reduces the average computation time for DR severity per image from 59.54 to 3.46 s.
Anonymous Payment in a Kiosk Centric Model using Digital signature scheme with message recovery and Low Computational Power Devices

In this paper we present an anonymous protocol for a mobile payment system based on a Kiosk Centric Case Mobile Scenario where the customer cannot communicate with the issuer due to absence of Internet access with her mobile device and the costs of implementing other mechanism of communication between both of them are high. Our protocol protects the real identity of the clients during the purchase and employs a digital signature scheme with message recovery using self-certified public keys that reduces the public space and the communication cost in comparison with the certificate-based signature schemes. Moreover, our proposed protocol requires low computational power that makes it suitable for mobile devices. As a result, our proposal illustrates how a portable device equipped with a short range link (such Bluetooth, Infrared or Wi-Fi) and low computational power should be enough to interact with a vendor machine in order to buy goods or services in a secure way.
MobiCloud: A geo-distributed mobile cloud computing platform

In a cloud computing environment, users prefer to migrate their locally processing workloads onto the cloud where more resources with better performance can be expected. ProtoGENI  and PlanetLab  have further improved the current Internet-based resource outsourcing by allowing end users to construct a virtual network system through virtualization and programmable networking technologies. However, to the best of our knowledge, there is no such general service or resource provisioning platform designated for mobile devices. In this paper, we present a new design and implementation of MobiCloud that is a geo-distributed mobile cloud computing platform. The discussions of the system components, infrastructure, management, implementation flow, and service scenarios are followed by an example on how to experience the MobiCloud system.
On the Interpolation of Data with Normally Distributed Uncertainty for Visualization

In many fields of science or engineering, we are confronted with uncertain data. For that reason, the visualization of uncertainty received a lot of attention, especially in recent years. In the majority of cases, Gaussian distributions are used to describe uncertain behavior, because they are able to model many phenomena encountered in science. Therefore, in most applications uncertain data is (or is assumed to be) Gaussian distributed. If such uncertain data is given on fixed positions, the question of interpolation arises for many visualization approaches. In this paper, we analyze the effects of the usual linear interpolation schemes for visualization of Gaussian distributed data. In addition, we demonstrate that methods known in geostatistics and machine learning have favorable properties for visualization purposes in this case.
OpenWSN: a standards-based low-power wireless development environment

The OpenWSN project is an open-source implementation of a fully standar ds-based protocol stack for capillary networks, rooted in the new IEEE802.15.4e Time Synchronized Channel Hopping standard. IEEE802.15.4e, coupled with Internetof-Things standards, such as 6LoWPAN, RPL and CoAP, enables ultra -low power and highly reliable mesh networks which are fully integrated into the Internet. The resulting protocol stack will be cor nerstone to the upcoming Machine-to-Machine revolution. This article gives an overview of the protocol stack, as well key integratio n details and the platforms and tools developed around it. The pure C OpenWSN stack was ported to four off-the-shelf p latforms representative of hardware currently used, from older 16-bit micro-controller to state-of-the-art 32-bit Co rtex-M architectures. The tools developed around the low-power mesh networks include visualization and debugging software, a simulator to mimic OpenWSN networks on a PC, and the environment needed to connect those networks to the Intern et. Experimental results presented in this article include a network where motes operate at an average radio duty cycle well below 0.1% and an average current draw of 68 μA on off-the-shelf hardware. These ultra-low power requirements ena bl a range of applications, with motes perpetually powered by micro-scaven ging devices. OpenWSN is, to the best of our knowledge, the first open-source implementation of the IEEE802.15.4 e standard.
Pavlov: Programming by Stimulus-Response Demonstration

Pavlov is a Programming By Demonstration (PBD) system that allows animated interfaces to be created without programming. Using a drawing editor and a clock, designers specify the behavior of a target interface by demonstrating stimuli (end-user actions or time) and the (time-stamped) graphical transformations that should be executed in response. This stimulus-response model allows interaction and animation to be defined in a uniform manner, and it allows for the demonstration of interactive animation, i.e., game-like behaviors in which the end-user (player) controls the speed and direction of object movement.
Integrating financial and physical supply chains : the role of banks in enabling supply chain integration

The financial supply chain, running parallel to the flow of goods and information, is common to all economic supply networks, and its integration with the physical supply chain is therefore a critical and ubiquitous aspect of supply chain integration (SCI) largely ignored in the literature. This paper aims to develop a model of physical and financial SCI, which is based on a process view from both buyers’ and suppliers’ perspectives, and explores the role of banks in enabling SCI.  The paper reports an exploratory study of the role of banks in improving SCI, by presenting a case study analysis of two international banks.  The findings show that banks can support buyers and suppliers by contributing to the enablers of SCI, namely coordination, collaboration, information sharing and information visibility.  The research is limited in that it is explorative; further studies are required in order to quantify the impact of banks’ interventions on SCI.  Improved SCI requires an understanding of the flow of physical and financial resources across supply networks. Banks can help buyers and suppliers develop a more holistic understanding of the supply chain, thus improving integration and optimising working capital.  The paper presents a process model of physical/financial SCI which uniquely recognises the role of banks in enabling buyers and suppliers to improve SCI, synchronisation and performance.
FACTORS AFFECTING CORPORATE SOCIAL RESPONSIBILITY DISCLOSURE IN EGYPT

The study makes a significant contribution to the corporate social responsibility (CSR) disclosure literature by offering the first study of its type undertaken in Egypt as an example of a developing country that examines the determinants of individual and aggregated types of CSR information. Using a sample of 111 Egyptian listed companies for the period of 2005–2010, we find that 66% of the Egyptian listed companies disclose on average 10–50 CSR statements. In addition, we find that product/customer information is used extensively by Egyptian listed companies compared with other types of CSR information. Finally we find that profitability is the main determinant for the aggregated and most of individual CSR information in Egypt.
The Identity Crisis Within the IS Discipline: Defining and Communicating the Discipline's Core Properties

We are concerned that the IS research community is making the discipline's central identity ambiguous by, all too frequently, under-investigating phenomena intimately associated with IT-based systems and over-investigating phenomena distantly associated with IT-based systems. In this 1 Ron Weber was the accepting senior editor for this paper. commentary, we begin by discussing why establishing an identity for the IS field is important. We then describe what such an identity may look like by proposing a core set of properties, i.e., concepts and phenomena, that define the IS field. Next, we discuss research by IS scholars that either fails to address this core set of properties (labeled as error of exclusion) or that addresses concepts/phenomena falling outside this core set (labeled as error of inclusion). We conclude by offering suggestions for redirecting IS scholarship toward the concepts and phenomena that we argue define the core of the IS discipline.
Increased auditory cortical representation in musicians

Acoustic stimuli are processed throughout the auditory projection pathway, including the neocortex, by neurons that are aggregated into ‘tonotopic’ maps according to their specific frequency tunings. Research on animals has shown that tonotopic representations are not statically fixed in the adult organism but can reorganize after damage to the cochlea or after training the intact subject to discriminate between auditory stimuli. Here we used functional magnetic source imaging (single dipole model) to measure cortical representations in highly skilled musicians. Dipole moments for piano tones, but not for pure tones of similar fundamental frequency (matched in loudness), were found to be enlarged by about 25% in musicians compared with control subjects who had never played an instrument. Enlargement was correlated with the age at which musicians began to practise and did not differ between musicians with absolute or relative pitch. These results, when interpreted with evidence for modified somatosensory representations of the fingering digits in skilled violinists, suggest that use-dependent functional reorganization extends across the sensory cortices to reflect the pattern of sensory input processed by the subject during development of musical skill.
Modelling of Mechanical and Mechatronic Systems MMaMS 2014 Pressure Distribution in Transtibial Prostheses Socket and the Stump Interface

The paper deals with the problem of a biomechanical solution for human locomotion after amputation of a lower limb. With an inappropriately designed transtibial prostheses socket, the interaction between the socket and the stump occurs, leading to increased friction and subsequent surface damage to the soft tissue. In individual types of transtibial prostheses, locations are defined which can be loaded and which cannot be loaded. On the basis of the study of the anterior side of the stump, three loadable and two non-loadable areas were monitored using the TACTILUS tactile pressure sensor (Sensor Products Inc., Madison, New Jersey, USA). Methods of measuring individual values were proposed for the purpose of sensing the pressure in the socket and the liner interface, the stump and the liner interface. Each method includes the methodological preparation of the patient, the preparation of the room, the preparation of the measuring system, and the subsequent method of data processing. In the submitted paper, 11 stumps were non-invasively monitored. The method of assessing the pressure exerted on the stumps was focused on the monitoring of the pressure distribution in the selected areas. The results obtained by the pressure measurements were statistically processed. In both cases of the pressure system placement, the p-value was higher than 0.05; therefore, we can state that the sets are equal.
Adhesive capsulitis: sonographic changes in the rotator cuff interval with arthroscopic correlation

To evaluate the sonographic findings of the rotator interval in patients with clinical evidence of adhesive capsulitis immediately prior to arthroscopy. We prospectively compared 30 patients with clinically diagnosed adhesive capsulitis (20 females, 10 males, mean age 50 years) with a control population of 10 normal volunteers and 100 patients with a clinical suspicion of rotator cuff tears. Grey-scale and colour Doppler sonography of the rotator interval were used. Twenty-six patients (87%) demonstrated hypoechoic echotexture and increased vascularity within the rotator interval, all of whom had had symptoms for less than 1 year. Three patients had hypoechoic echotexture but no increase in vascularity, and one patient had a normal sonographic appearance. All patients were shown to have fibrovascular inflammatory soft-tissue changes in the rotator interval at arthroscopy commensurate with adhesive capsulitis. None of the volunteers or the patients with a clinical diagnosis of rotator cuff tear showed such changes. Sonography can provide an early accurate diagnosis of adhesive capsulitis by assessing the rotator interval for hypoechoic vascular soft tissue.
Online movement adaptation based on previous sensor experiences

Personal robots can only become widespread if they are capable of safely operating among humans. In uncertain and highly dynamic environments such as human households, robots need to be able to instantly adapt their behavior to unforseen events. In this paper, we propose a general framework to achieve very contact-reactive motions for robotic grasping and manipulation. Associating stereotypical movements to particular tasks enables our system to use previous sensor experiences as a predictive model for subsequent task executions. We use dynamical systems, named Dynamic Movement Primitives (DMPs), to learn goal-directed behaviors from demonstration. We exploit their dynamic properties by coupling them with the measured and predicted sensor traces. This feedback loop allows for online adaptation of the movement plan. Our system can create a rich set of possible motions that account for external perturbations and perception uncertainty to generate truly robust behaviors. As an example, we present an application to grasping with the WAM robot arm.
Exploiting Privileged Information from Web Data for Image Categorization

Relevant and irrelevant web images collected by tag-based image retrieval have been employed as loosely labeled training data for learning SVM classifiers for image categorization by only using the visual features. In this work, we propose a new image categorization method by incorporating the textual features extracted from the surrounding textual descriptions (tags, captions, categories, etc.) as privileged information and simultaneously coping with noise in the loose labels of training web images. When the training and test samples come from different datasets, our proposed method can be further extended to reduce the data distribution mismatch by adding a regularizer based on the Maximum Mean Discrepancy (MMD) criterion. Our comprehensive experiments on three benchmark datasets demonstrate the effectiveness of our proposed methods for image categorization and image retrieval by exploiting privileged information from web data.
In Defense of One-Vs-All Classification

We consider the problem of multiclass classification. Our main thesis is that a simple “one-vs-all” scheme is as accurate as any other approach, assuming that the underlying binary classifiers are well-tuned regularized classifiers such as support vector machines. This thesis is interesting in that it disagrees with a large body of recent published work on multiclass classification. We support our position by means of a critical review of the existing literature, a substantial collection of carefully controlled experimental work, and theoretical arguments.
A Personalized Markov Clustering and Deep Learning Approach for Arabic Text Categorization

Text categorization has become a key research field in the NLP community. However, most works in this area are focused on Western languages ignoring other Semitic languages like Arabic. These languages are of immense political and social importance necessitating robust categorization techniques. In this paper, we present a novel three-stage technique to efficiently classify Arabic documents into different categories based on the words they contain. We leverage the significance of root-words in Arabic and incorporate a combination of Markov clustering and Deep Belief Networks to classify Arabic words into separate groups (clusters). Our approach is tested on two public datasets giving a F-Measure of 91.02%.
An Examination of Undergraduate Student's Perceptions and Predilections of the Use of YouTube in the Teaching and Learning Process

Pervasive social networking and media sharing technologies have augmented perceptual understanding and information gathering and, while text-based resources have remained the standard for centuries, they do not appeal to the hyper-stimulated visual learners of today. In particular, the research suggests that targeted YouTube videos enhance student engagement, depth of understanding, and overall satisfaction in higher education courses. In order to investigate student perceptions and preferences regarding the implications of YouTube, a study was conducted at a Mid-Atlantic minority serving institution that examined student opinions regarding the usage of YouTube videos to augment instruction in online and classroombased courses. According to the findings, use of YouTube in the teaching and learning process enhances instruction with students most likely to visit video sharing services from mobile devices. Further, length has an impact on student decisions whether or not to watch a video, and course delivery format impacts length and audio preferences. Finally, there is no relationship between personal use of social media and the perceived value of the use of YouTube in the instructional process.
Biodegradation of lignocellulosics: microbial, chemical, and enzymatic aspects of the fungal attack of lignin.

Wood is the main renewable material on Earth and is largely used as building material and in paper-pulp manufacturing. This review describes the composition of lignocellulosic materials, the different processes by which fungi are able to alter wood, including decay patterns caused by white, brown, and soft-rot fungi, and fungal staining of wood. The chemical, enzymatic, and molecular aspects of the fungal attack of lignin, which represents the key step in wood decay, are also discussed. Modern analytical techniques to investigate fungal degradation and modification of the lignin polymer are reviewed, as are the different oxidative enzymes (oxidoreductases) involved in lignin degradation. These include laccases, high redox potential ligninolytic peroxidases (lignin peroxidase, manganese peroxidase, and versatile peroxidase), and oxidases. Special emphasis is given to the reactions catalyzed, their synergistic action on lignin, and the structural bases for their unique catalytic properties. Broadening our knowledge of lignocellulose biodegradation processes should contribute to better control of wood-decaying fungi, as well as to the development of new biocatalysts of industrial interest based on these organisms and their enzymes.
Shuffle and Learn: Unsupervised Learning Using Temporal Order Verification

In this paper, we present an approach for learning a visual representation from the raw spatiotemporal signals in videos. Our representation is learned without supervision from semantic labels. We formulate our method as an unsupervised sequential verification task, i.e., we determine whether a sequence of frames from a video is in the correct temporal order. With this simple task and no semantic labels, we learn a powerful visual representation using a Convolutional Neural Network (CNN). The representation contains complementary information to that learned from supervised image datasets like ImageNet. Qualitative results show that our method captures information that is temporally varying, such as human pose. When used as pre-training for action recognition, our method gives significant gains over learning without external data on benchmark datasets like UCF101 and HMDB51. To demonstrate its sensitivity to human pose, we show results for pose estimation on the FLIC and MPII datasets that are competitive, or better than approaches using significantly more supervision. Our method can be combined with supervised representations to provide an additional boost in accuracy.
Seeing Stars of Valence and Arousal in Blog Posts

Sentiment analysis is a growing field of research, driven by both commercial applications and academic interest. In this paper, we explore multiclass classification of diary-like blog posts for the sentiment dimensions of valence and arousal, where the aim of the task is to predict the level of valence and arousal of a post on a ordinal five-level scale, from very negative/low to very positive/high, respectively. We show how to map discrete affective states into ordinal scales in these two dimensions, based on the psychological model of Russell's circumplex model of affect and label a previously available corpus with multidimensional, real-valued annotations. Experimental results using regression and one-versus-all approaches of support vector machine classifiers show that although the latter approach provides better exact ordinal class prediction accuracy, regression techniques tend to make smaller scale errors.