nils-herrmann/clean_scirepeval huggingface dataset:
A Novel IoT Authorization Architecture on Hyperledger Fabric With Optimal Consensus Using Genetic Algorithm

This paper proposes a distributed authorization architecture for Internet of Things adopting a hyperledger fabric framework. This paper also applies Genetic Algorithm to enhance the consensus component, traditionally used for Kafka, in order to determine the best configuration over different parameters, such as input transactions and their success rates. The experimental results confirm our superior performance in that the transaction transfer rate of our optimization, called GA Kafka, outperforms the traditional Kafka by 69.43%, and 89.97% for transaction transfer success rate.
Instant Dehazing of Images Using Polarization

We present an approach to easily remove the effects of haze from images. It is based on the fact that usually airlight scattered by atmospheric particles is partially polarized. Polarization filtering alone cannot remove the haze effects, except in restricted situations. Our method, however, works under a wide range of atmospheric and viewing conditions. We analyze the image formation process, taking into account polarization effects of atmospheric scattering. We then invert the process to enable the removal of haze from images. The method can be used with as few as two images taken through a polarizer at different orientations. This method works instantly, without relying on changes of weather conditions. We present experimental results of complete dehazing in far from ideal conditions for polarization filtering. We obtain a great improvement of scene contrast and correction of color. As a by product, the method also yields a range (depth) map of the scene, and information about properties of the atmospheric particles.
The Taste for Privacy: An Analysis of College Student Privacy Settings in an Online Social Network

The rapid growth of contemporary social network sites (SNSs) has coincided with an increasing concern over personal privacy. College students and adolescents routinely provide personal information on profiles that can be viewed by large numbers of unknown people and potentially used in harmful ways. SNSs like Facebook and MySpace allow users to control the privacy level of their profile, thus limiting access to this information. In this paper, we take the preference for privacy itself as our unit of analysis, and analyze the factors that are predictive of a student having a private versus public profile. Drawing upon a new social network dataset based on Facebook, we argue that privacy behavior is an upshot of both social influences and personal incentives. Students are more likely to have a private profile if their friends and roommates have them; women are more likely to have private profiles than are men; and having a private profile is associated with a higher level of online activity. Finally, students who have private versus public profiles are characterized by a unique set of cultural preferences—of which the ‘‘taste for privacy’’ may be only a small but integral part.
Note-Taking and Secondary Students with Learning Disabilities : Challenges and Solutions

As more secondary students with learning disabilities (LD) enroll in advanced content-area classes and are expected to pass state exams, they are faced with the challenge of mastering difficult concepts and abstract vocabulary while learning content. Once in these classes, students must learn from lectures that move at a quick pace, record accurate and complete notes, and then demonstrate their mastery of the content on tests. This article provides an overview of the challenges faced by students with LD in content-area classes and discusses the problems that students have learning from lectures and recording notes. Further, the article discusses theory and research related to note-taking and presents note-taking interventions that teachers can use to help students improve their note-taking skills, and ultimately, improve their achievement in these classes.
Efficient Image Evidence Analysis of CNN Classification Results

Convolutional neural networks (CNNs) define the current state-of-the-art for image recognition. With their emerging popularity, especially for critical applications like medical image analysis or self-driving cars, confirmability is becoming an issue. The black-box nature of trained predictors make it difficult to trace failure cases or to understand the internal reasoning processes leading to results. In this paper we introduce a novel efficient method to visualise evidence that lead to decisions in CNNs. In contrast to network fixation or saliency map methods, our method is able to illustrate the evidence for or against a classifier’s decision in input pixel space approximately 10/times faster than previous methods. We also show that our approach is less prone to noise and can focus on the most relevant input regions, thus making it more accurate and interpretable. Moreover, by making simplifications we link our method with other visualisation methods, providing a general explanation for gradient-based visualisation techniques. We believe that our work makes network introspection more feasible for debugging and understanding deep convolutional networks. This will increase trust between humans and deep learning models.
Exact algorithms for routing problems under vehicle capacity constraints

The solution of a vehicle routing problem calls for the determination of a set of routes, each performed by a single vehicle which starts and ends at its own depot, such that all the requirements of the customers are fulfilled and the global transportation cost is minimized. The routes have to satisfy several operational constraints which depend on the nature of the transported goods, on the quality of the service level, and on the characteristics of the customers and of the vehicles. One of the most common operational constraint addressed in the scientific literature is that the vehicle fleet is capacitated and the total load transported by a vehicle cannot exceed its capacity. This paper provides a review of the most recent developments that had a major impact in the current state-of-the-art of exact algorithms for vehicle routing problems under capacity constraints, with a focus on the basic Capacitated Vehicle Routing Problem (CVRP) and on heterogeneous vehicle routing problems. The most important mathematical formulations for the problem together with various relaxations are reviewed. The paper also describes the recent exact methods and reports a comparison of their computational performances.
An edge-set based large scale graph processing system

Next generation analytics will be all about graphs, though performance has been a fundamental challenge for large scale graph processing. In this paper, we present an industrial graph processing engine for exploring various large scale linked data, which exhibits superior performance due to the several innovations. This engine organizes a graph as a set of edge-sets, compatible with the traditional edge-centric sharding for graphs, but becomes more amenable for large scale processing. Each time only a portion of the sets are needed for computation and the data access patterns can be highly predictable for prefetch for many graph computing algorithms. Due to the sparsity of large scale graph structure, this engine differentiates logical edge-sets from the edge-sets physically stored on the disk, where multiple logical edge-sets can be organized into a same physical edge-set to increase the data locality. Besides, in contrast to existing solution, the data structures utilized for the physical edge-sets can vary from one to another. Such heterogeneous edge-set representation explores the best graph processing performance according to local data access patterns. We conduct experiments on a representative set of property graphs on multiple platforms, where the proposed system outperform the baseline systems consistently.
A RBF neural network model for anti-money laundering

Money laundering (ML) is a serious crime which makes it necessary to develop detection methods in transactions. Some researches have been carried on, but the problem is not thoroughly solved. Aiming at the low detection rate of suspicious transaction at home and abroad in financial field, and with an analysis of radial basis function (RBF) neural network, we propose a radial basis function neural network model based on APC-III clustering algorithm and recursive least square algorithm for anti-money laundering (AML). APC-III clustering algorithm is used for determining the parameters of radial basis function in hidden layer, and recursive least square (RLS) algorithm is adopted to update weights of connections between hidden layer and output layer. The proposed method is compared against support vector machine (SVM) and outlier detection methods, which show that the proposed method has the highest detection rate and the lowest false positive rate. Thus our method is proved to have both theoretical and practical value for anti-money laundering.
Types of Participant Behavior in a Massive Open Online Course

In recent years there has been a proliferation of massive open online courses (MOOCs), which provide unprecedented opportunities for lifelong learning. Registrants approach these courses with a variety of motivations for participation. Characterizing the different types of participation in MOOCs is fundamental in order to be able to better evaluate the phenomenon and to support MOOCs developers and instructors in devising courses which are adapted for different learners' needs. Thus, the purpose of this study was to characterize the different types of participant behavior in a MOOC. Using a data mining methodology, 21,889 participants of a MOOC were classified into clusters, based on their activity in the main learning resources of the course: video lectures, discussion forums, and assessments. Thereafter, the participants in each cluster were characterized in regard to demographics, course participation, and course achievement characteristics. Seven types of participant behavior were identified: Tasters (64.8%), Downloaders (8.5%), Disengagers (11.5%), Offline Engagers (3.6%), Online Engagers (7.4%), Moderately Social Engagers (3.7%), and Social Engagers (0.6%). A significant number of 1,020 participants were found to be engaged in the course, but did not achieve a certificate. The types are discussed according to the established research questions. The results provide further evidence regarding the utilization of the flexibility, which is offered in MOOCs, by the participants according to their needs. Furthermore, this study supports the claim that MOOCs' impact should not be evaluated solely based on certification rates but rather based on learning behaviors.
MTPV for Continuous Flux-Weakening Strategy Control Law for IPMSM

Synchronous motors with Interior Permanent Magnets (IPMSM) are particularly effective for operation at high speed due to their high power density. However the speeds generate large electromagnetic forces and it is necessary to set up a flux-weakening. Classical control algorithms do flux-weakening by injecting a negative $\mathbf{I}_{\mathrm{d}}$ current in respect of current norm. The value of this current is usually defined on the LUTs (Look Up Tables) depending on the speed and the torque required reflecting open-loop operation. This article presents continuous control law in closed loop ensuring the operation from the speed zero up to the maximum speed. The generation of the current reference $\mathbf{I}_{\mathrm{d}}$ is based on strategy to Minimum Current per Torque (MinCPT) followed a Maximum Torque per Volt (MTPV) strategy. The goal is to maximize the speed for a given power. The effectiveness of this unified algorithm is tested on an experimental platform.
An evolutionary tic-tac-toe player

In this paper, artificial neural networks are used as function evaluators in order to evolve game playing strategies for the game of tic-tac-toe. The best evolved player is tested against an online perfect tic tac toe player, and also against a nearly perfect player which allows 10% random moves and finally against five selected human players. Those players are with different playing abilities. The results are promising, suggesting many other research directions.
Tracking Actual Usage: the Attention Metadata Approach

The information overload in learning and teaching scenarios is a main hindering factor for efficient and effective learning. New methods are needed to help teachers and students in dealing with the vast amount of available information and learning material. Our approach aims to utilize contextualized attention metadata to capture behavioral information of users in learning contexts that can be used to deal with the information overload in user centric ways. We introduce a schema and framework for capturing and managing such contextualized attention metadata in this paper. Schema and framework are designed to enable collecting and merging observations about the attention users give to content and their context. The contextualized attention metadata schema enables the correlation of the observations, thus reflects the relationships that exists between the user, her context and the content she works with. We illustrate with a simple demo application how contextualized attention metadata can be collected from several tools, the merging of the data streams into a repository and finally the correlation of the data.
Multiuser Precoding With Limited Cooperation for Large-Scale MIMO Multicell Downlink

Multiple base stations (BSs) equipped with large antenna arrays can perform cooperative downlink transmissions using beamforming for cell-edge users to provide high data rate services. However, due to an excessive overhead for the channel state information (CSI) exchange between BSs, the cooperation with full CSI may not be practical for large antenna arrays. Thus, based on an existing precoding approach, two different precoding approaches with partial cooperation, where no CSI exchange is required, are proposed. One of them (called Type III precoding) has a better performance, and its performance can be further improved by user allocation, which becomes possible by deriving a closed-form expression for transmission power. For low-complexity implementations, an algorithm is derived for the user allocation based on the greedy algorithm. Under certain channel conditions, closed-form expressions for average transmission powers are also derived to see asymptotic performance. Based on them, we can show that the average transmission power becomes Q times higher than that with full cooperation if precoding is carried out with local CSI only, where Q is the number of BSs in cooperation, which would be considered as a penalty due to partial cooperation. Through simulations, it is shown that this penalty (i.e., the increase of transmission power) can be reduced by user allocation.
An Improved Rate Matching Algorithm for 3GPP LTE Turbo Code

Long Term Evolution (LTE) describes the standardization work by the Third Generation Partnership Project (3GPP) to define a new high-speed radio access method for mobile communications systems. Rate Matching (RM) is an essential component in the adaptive coding rate system and has a great impact on the error correcting performance in turbo code system for 3GPP LTE. In this paper, we propose an efficient RM algorithm based on the circular buffer and interlaced puncturing applying to the 3GPP LTE system. More specifically, both system-parity cross output model of turbo code and puncturing parity bits preferentially pattern in the bit collection and puncturing process are introduced to obtain more stable performance in this RM algorithm. The simulation results indicate that the proposed algorithm can effectively improve the bit error ratio (BER) performance, especially at high code rate conditions, compared with traditional algorithm in LTE turbo code.
Effects of wine, alcohol and polyphenols on cardiovascular disease risk factors: evidences from human studies.

The aim of this review was to focus on the knowledge of the cardiovascular benefits of moderate alcohol consumption, as well as to analyze the effects of the different types of alcoholic beverages.



Systematic revision of human clinical studies and meta-analyses related to moderate alcohol consumption and cardiovascular disease (CVD) from 2000 to 2012.



Heavy or binge alcohol consumption unquestionably leads to increased morbidity and mortality. Nevertheless, moderate alcohol consumption, especially alcoholic beverages rich in polyphenols, such as wine and beer, seems to confer cardiovascular protective effects in patients with documented CVD and even in healthy subjects.



In conclusion, wine and beer (but especially red wine) seem to confer greater cardiovascular protection than spirits because of their polyphenolic content. However, caution should be taken when making recommendations related to alcohol consumption.
PLC security and critical infrastructure protection

Programmable Logic Controllers (PLCs) are the most important components embedded in Industrial Control Systems (ICSs). ICSs have achieved highest standards in terms of efficiency and performance. As a result of that, higher portion of infrastructure in industries has been automated for the comfort of human beings. Therefore, protection of such systems is crucial. It is important to investigate the vulnerabilities of ICSs in order to solve the threats and attacks against critical infrastructure to protect human lives and assets. PLC is the basic building block of an ICS. If PLCs are exploited, overall system will be exposed to the threat. Many believed that PLCs are secured devices due to its isolation from the external networks of the system. The attacks such as Stuxnet have proven the incorrectness of such thoughts. In this paper we have revealed the vulnerabilities of PLCs through a variety of attack vectors which could affect the related critical infrastructure. Furthermore, we have proposed solutions for such weaknesses in PLC based systems.
Evaluation of highly available and fault-tolerant middleware clustered architectures using RabbitMQ

The paper presents a performance evaluation of message broker system, Rabbit MQ in high availability - enabling and redundant configurations. Rabbit MQ is a message queuing system realizing the middleware for distributed systems that implements the Advanced Message Queuing Protocol. The scalability and high availability design issues are discussed. Since HA and performance scalability requirements are in conflict, scenarios for using clustered RabbitMQ nodes and mirrored queues are presented. The results of performance measurements are reported.
Pitch Extraction and Fundamental Frequency: History and Current Techniques

Pitch extraction (also called fundamental frequency estimation) has been a popular topic in many fields of research since the age of computers. Yet in the course of some 50 years of study, current techniques are still not to a desired level of accuracy and robustness. When presented with a single clean pitched signal, most techniques do well, but when the signal is noisy, or when there are multiple pitch streams, many current pitch algorithms still fail to perform well. This report presents a discussion of the history of pitch detection techniques, as well as a survey of the current state of the art in pitch detection technology.
Styling with Attention to Details

Fashion as characterized by its nature, is driven by style. In this paper, we propose a method that takes into account the style information to complete a given set of selected fashion items with a complementary fashion item. Complementary items are those items that can be worn along with the selected items according to the style. Addressing this problem facilitates in automatically generating stylish fashion ensembles leading to a richer shopping experience for users. Recently, there has been a surge of online social websites where fashion enthusiasts post the outfit of the day and other users can like and comment on them. These posts contain a gold-mine of information about style. In this paper, we exploit these posts to train a deep neural network which captures style in an automated manner. We pose the problem of predicting complementary fashion items as a sequence to sequence problem where the input is the selected set of fashion items and the output is a complementary fashion item based on the style information learned by the model. We use the encoder decoder architecture to solve this problem of completing the set of fashion items. We evaluate the goodness of the proposed model through a variety of experiments. We empirically observe that our proposed model outperforms competitive baseline like apriori algorithm by ~28% in terms of accuracy for top-1 recommendation to complete the fashion ensemble. We also perform retrieval based experiments to understand the ability of the model to learn style and rank the complementary fashion items and find that using attention in our encoder decoder model helps in improving the mean reciprocal rank by ~24%. Qualitatively we find the complementary fashion items generated by our proposed model are richer than the apriori algorithm.
Cognitive advantage in bilingualism: an example of publication bias?

It is a widely held belief that bilinguals have an advantage over monolinguals in executive-control tasks, but is this what all studies actually demonstrate? The idea of a bilingual advantage may result from a publication bias favoring studies with positive results over studies with null or negative effects. To test this hypothesis, we looked at conference abstracts from 1999 to 2012 on the topic of bilingualism and executive control. We then determined which of the studies they reported were subsequently published. Studies with results fully supporting the bilingual-advantage theory were most likely to be published, followed by studies with mixed results. Studies challenging the bilingual advantage were published the least. This discrepancy was not due to differences in sample size, tests used, or statistical power. A test for funnel-plot asymmetry provided further evidence for the existence of a publication bias.
A Ka-band waveguide-based traveling-wave spatial power divider/combiner

A Ka-band waveguide-based four-stage eight-way power divider/combiner is presented based on the traveling-wave technique and the spatial power combining technique. The dualprobe, which are symmetrically inserted in one broadside of the waveguide, double the power dividing ways without adding the insertion loss. The method of images is used to simplify the analysis and design process of the dual-probe structure. The power divider/combiner is designed, fabricated and measured to demonstrate its wide bandwidth and low insertion loss performance. From 30GHz to 40GHz, the measured return loss of the back-to-back structure is lower than -15dB and the insertion loss is less than