nils-herrmann/clean_scirepeval huggingface dataset:
Evaluating Usability of E-Learning Systems in Universities

The use of e-learning systems has increased significantly in the recent times. E-learning systems are supplementing teaching and learning in universities globally. Kenyan universities have adopted e-learning technologies as means for delivering course content. However despite adoption of these systems, there are considerable challenges facing the usability of the systems. Lecturers and students have different perceptions in regard to the usability of e-learning systems. The aim of this study was to evaluate usability attributes that affect elearning systems in Kenyan universities. The study had two fold objectives; determining status of e-learning platforms and evaluating usability issues affecting e-learning adoption in Kenyan universities. The research took a case study of one of the public universities which has implemented Moodle e-learning system. The usability attributes evaluated were user-friendliness, learnability, technological infrastructure and policy. The research made recommendations which could help universities accelerate the adoption of e-learning systems. e-learning; moodle; usability; learnability
Where in the World are You? Geolocation and Language Identification in Twitter

The movements of ideas and content between locations and languages are unquestionably crucial concerns to researchers of the information age, and Twitter has emerged as a central, global platform on which hundreds of millions of people share knowledge and information. A variety of research has attempted to harvest locational and linguistic metadata from tweets in order to understand important questions related to the 300 million tweets that flow through the platform each day. However, much of this work is carried out with only limited understandings of how best to work with the spatial and linguistic contexts in which the information was produced. Furthermore, standard, well-accepted practices have yet to emerge. As such, this paper studies the reliability of key methods used to determine language and location of content in Twitter. It compares three automated language identification packages to Twitter’s user interface language setting and to a human coding of languages in order to identify common sources of disagreement. The paper also demonstrates that in many cases user-entered profile locations differ from the physical locations users are actually tweeting from. As such, these open-ended, user-generated, profile locations cannot be used as useful proxies for the physical locations from which information is published to Twitter.
Top-cited Articles in Chemical Engineering in Science Citation Index Expanded : A Bibliometric Analysis

This study aimed to identify and to analyze characteristics of top-cited articles published in the Web of Science chemical engineering subject category from 1899 to 2011. Articles that have been cited more than 100 times were assessed regarding publication outputs, and distribution of outputs in journals. Five bibliometric indicators were used to evaluate source countries, institution and authors. A new indicator, Y-index, was created to assess quantity and quality of contribution to articles. Results showed that 3828 articles, published between 1931 and 2010, had been cited at least 100 times. Among them 54% published before 1991, and 49% top-cited articles originated from US. The top eight productive institutions were all located in US. The top journals were Journal of Catalysis,  Y-index was successfully applied to evaluate publication character of authors, institutions, and countries/regions.
Fingerprint authentication using geometric features

Biometric based authentication, particularly for fingerprint authentication systems play a vital role in identifying an individual. The existing fingerprint authentication systems depend on specific points known as minutiae for recognizing an individual. Designing a reliable automatic fingerprint authentication system is still very challenging, since not all fingerprint information is available. Further, the information obtained is not always accurate due to cuts, scars, sweat, distortion and various skin conditions. Moreover, the existing fingerprint authentication systems do not utilize other significant minutiae information, which can improve the accuracy. Various local feature detectors such as Difference-of-Gaussian, Hessian, Hessian Laplace, Harris Laplace, Multiscale Harris, and Multiscale Hessian have been extensively used for feature detection. However, these detectors have not been employed for detecting fingerprint image features. In this article, a versatile local feature fingerprint matching scheme is proposed. The local features are obtained by exploiting these local geometric detectors and SIFT descriptor. This scheme considers local characteristic features of the fingerprint image, thus eliminating the issues caused in existing fingerprint feature based matching techniques. Computer simulations of the proposed algorithm on specific databases show significant improvements when compared to existing fingerprint matchers, such as minutiae matcher, hierarchical matcher and graph based matcher. Computer simulations conducted on the Neurotechnology database demonstrates a very low Equal Error Rate (EER) of 0.8%. The proposed system a) improves the accuracy of the fingerprint authentication system, b) works when the minutiae information is sparse, and c) produces satisfactory matching accuracy in the case when minutiae information is unavailable. The proposed system can also be employed for partial fingerprint authentication.
A Riemannian Network for SPD Matrix Learning

Symmetric Positive Definite (SPD) matrix learning methods have become popular in many image and video processing tasks, thanks to their ability to learn appropriate statistical representations while respecting Riemannian geometry of underlying SPD manifolds. In this paper we build a Riemannian network architecture to open up a new direction of SPD matrix non-linear learning in a deep model. In particular, we devise bilinear mapping layers to transform input SPD matrices to more desirable SPD matrices, exploit eigenvalue rectification layers to apply a non-linear activation function to the new SPD matrices, and design an eigenvalue logarithm layer to perform Riemannian computing on the resulting SPD matrices for regular output layers. For training the proposed deep network, we exploit a new backpropagation with a variant of stochastic gradient descent on Stiefel manifolds to update the structured connection weights and the involved SPD matrix data. We show through experiments that the proposed SPD matrix network can be simply trained and outperform existing SPD matrix learning and state-of-the-art methods in three typical visual classification tasks.
LPRNet: License Plate Recognition via Deep Neural Networks

This paper proposes LPRNet end-to-end method for Automatic License Plate Recognition without preliminary character segmentation. Our approach is inspired by recent breakthroughs in Deep Neural Networks, and works in real-time with recognition accuracy up to 95% for Chinese license plates: 3 ms/plate on nVIDIA R © GeForceTMGTX 1080 and 1.3 ms/plate on Intel R © CoreTMi7-6700K CPU. LPRNet consists of the lightweight Convolutional Neural Network, so it can be trained in end-to-end way. To the best of our knowledge, LPRNet is the first real-time License Plate Recognition system that does not use RNNs. As a result, the LPRNet algorithm may be used to create embedded solutions for LPR that feature high level accuracy even on challenging Chinese license plates.
A Survey of Android Security Threats and Defenses

With billions of people using smartphones and the exponential growth of smartphone apps, it is prohibitive for app marketplaces, such as Google App Store, to thoroughly verify if an app is legitimate or malicious. As a result, mobile users are left to decide for themselves whether an app is safe to use. Even worse, recent studies have shown that over 70% of apps in markets request to collect data irrelevant to the main functions of the apps, which could cause leaking of private information or inefficient use of mobile resources. It is worth mentioning that since resource management mechanism of mobile devices is different from PC machines, existing security solutions in PC malware area are not quite compatible with mobile devices. Therefore, academic researchers and commercial anti-malware companies have proposed many security mechanisms to address the security issues of the Android devices. Considering the mechanisms and techniques which are different in nature and used in proposed works, they can be classified into different categories. In this survey, we discuss the existing Android security threats and existing security enforcements solutions between 2010−2015 and try to classify works and review their functionalities. We review a few works of each class. The survey also reviews the strength and weak points of the solutions.
Industrial Automation as a Cloud Service

New cloud services are being developed to support a wide variety of real-life applications. In this paper, we introduce a new cloud service: industrial automation, which includes different functionalities from feedback control and telemetry to plant optimization and enterprise management. We focus our study on the feedback control layer as the most time-critical and demanding functionality. Today's large-scale industrial automation projects are expensive and time-consuming. Hence, we propose a new cloud-based automation architecture, and we analyze cost and time savings under the proposed architecture. We show that significant cost and time savings can be achieved, mainly due to the virtualization of controllers and the reduction of hardware cost and associated labor. However, the major difficulties in providing cloud-based industrial automation systems are timeliness and reliability. Offering automation functionalities from the cloud over the Internet puts the controlled processes at risk due to varying communication delays and potential failure of virtual machines and/or links. Thus, we design an adaptive delay compensator and a distributed fault tolerance algorithm to mitigate delays and failures, respectively. We theoretically analyze the performance of the proposed architecture when compared to the traditional systems and prove zero or negligible change in performance. To experimentally evaluate our approach, we implement our controllers on commercial clouds and use them to control: (i) a physical model of a solar power plant, where we show that the fault-tolerance algorithm effectively makes the system unaware of faults, and (ii) industry-standard emulation with large injected delays and disturbances, where we show that the proposed cloud-based controllers perform indistinguishably from the best-known counterparts: local controllers.
A novel hybrid ( wind – photovoltaic ) system sizing procedure

Wind-photovoltaic hybrid system (WPHS) utilization is becoming popular due to increasing energy costs and decreasing prices of turbines and photovoltaic (PV) panels. However, prior to construction of a renewable generation station, it is necessary to determine the optimum number of PV panels and wind turbines for minimal cost during continuity of generated energy to meet the desired consumption. In fact, the traditional sizing procedures find optimum number of the PV modules and wind turbines subject to minimum cost. However, the optimum battery capacity is either not taken into account, or it is found by a full search between all probable solution spaces which requires extensive computation. In this study, a novel description of the production/consumption phenomenon is proposed, and a new sizing procedure is developed. Using this procedure, optimum battery capacity, together with optimum number of PV modules and wind turbines subject to minimum cost can be obtained with good accuracy.
A Systematic Method for Designing a PR Controller and Active Damping of the LCL Filter for Single-Phase Grid-Connected PV Inverters

The Proportional Resonant (PR) current controller provides gains at a certain frequency (resonant frequency) and eliminates steady state errors. Therefore, the PR controller can be successfully applied to single grid-connected PV inverter current control. On the contrary, a PI controller has steady-state errors and limited disturbance rejection capability. Compared with the Land LC filters, the LCL filter has excellent harmonic suppression capability, but the inherent resonant peak of the LCL filter may introduce instability in the whole system. Therefore, damping must be introduced to improve the control of the system. Considering the controller and the LCL filter active damping as a whole system makes the controller design method more complex. In fact, their frequency responses may affect each other. The traditional trial-and-error procedure is too time-consuming and the design process is inefficient. This paper provides a detailed analysis of the frequency response influence between the PR controller and the LCL filter regarded as a whole system. In addition, the paper presents a systematic method for designing controller parameters and the capacitor current feedback coefficient factor of LCL filter active-damping. The new method relies on meeting the stable margins of the system. Moreover, the paper also clarifies the impact of the grid on the inverter output current. Numerical simulation and a 3 kW laboratory setup assessed the feasibility and effectiveness of the proposed method.
The Influence of Partisan Motivated Reasoning on Public Opinion

Political parties play a vital role in democracies by linking citizens to their representatives. Nonetheless, a longstanding concern is that partisan identification slants decision-making. Citizens may support (oppose) policies that they would otherwise oppose (support) in the absence of an endorsement from a political party—this is due in large part to what is called partisan motivated reasoning where individuals interpret information through the lens of their party commitment. We explore partisan motivated reasoning in a survey experiment focusing on support for an energy law. We identify two politically relevant factors that condition partisan motivated reasoning: (1) an explicit inducement to form an ‘‘accurate’’ opinion, and (2) cross-partisan, but not consensus, bipartisan support for the law. We further provide evidence of how partisan motivated reasoning works psychologically and affects opinion strength. We conclude by discussing the implications of our results for understanding opinion formation and the overall quality of citizens’ opinions.
Pushover Analysis of R . C Frames Considering Soil Structure Interaction

Generally structures are analyzed for its structural behaviour assuming base condition as a fixed base. However it is observed that due to assumption of fixity at base condition, the results obtained are very much different from actual condition. In view of the above, while evaluating seismic structural behavior, the effect of soil structure interaction is of paramount importance. Therefore, in this study various structures are analyzed for fixed base as well as for various flexible base conditions, which are categorized as medium soil and soft soil. For analysis purpose in this study structures are considered from G+5 stories to G+25 stories at an interval of 5 stories. While assessing the seismic behaviour of structure, the pushover analysis is employed and accordingly its effect in the form of failure hinge formulation is studied.
Distributed Machine Learning-but at what COST ?

Training machine learning models at scale is a popular workload for distributed data flow systems. However, as these systems were originally built to fulfill quite different requirements it remains an open question how effectively they actually perform for ML workloads. In this paper we argue that benchmarking of large scale ML systems should consider state of the art, single machine libraries as baselines and sketch such a benchmark for distributed data flow systems. We present an experimental evaluation of a representative problem for XGBoost, LightGBM and Vowpal Wabbit and compare them to Apache Spark MLlib with respect to both: runtime and prediction quality. Our results indicate that while being able to robustly scale with increasing data set size, current generation data flow systems are surprisingly inefficient at training machine learning models at need substantial resources to come within reach of the performance of single machine libraries.
Big Data Analytics

With advancement of technologies and services, data with high velocity, variety and volume is produced which cannot be handled by traditional architectures, algorithms or databases. So, there is a need of new architecture that finds the hidden threads and trends from different structured or unstructured sources and that technique is called BIG DATA. This Review paper presents different Methodologies to implement Big Data that is HPCC (older one) and HADOOP. The whole process of deployment can be divided into 5 phasesData Distillation, Model Deployment, Validation and Deployment, Real time Scoring, Model Refresh. Along with that it concentrates on comparing HPCC , HADOOP and their components also.
Curiosity Driven Exploration of Learned Disentangled Goal Spaces

Intrinsically motivated goal exploration processes enable agents to autonomously sample goals to explore efficiently complex environments with high-dimensional continuous actions. They have been applied successfully to real world robots to discover repertoires of policies producing a wide diversity of effects. Often these algorithms relied on engineered goal spaces but it was recently shown that one can use deep representation learning algorithms to learn an adequate goal space in simple environments. However, in the case of more complex environments containing multiple objects or distractors, an efficient exploration requires that the structure of the goal space reflects the one of the environment. In this paper we show that using a disentangled goal space leads to better exploration performances than an entangled one. We further show that when the representation is disentangled, one can leverage it by sampling goals that maximize learning progress in a modular manner. Finally, we show that the measure of learning progress, used to drive curiosity-driven exploration, can be used simultaneously to discover abstract independently controllable features of the environment. The code used in the experiments is available at https://github.com/flowersteam/ Curiosity_Driven_Goal_Exploration.
Reading Comprehension with Graph-based Temporal-Casual Reasoning

Complex questions in reading comprehension tasks require integrating information from multiple sentences. In this work, to answer such questions involving temporal and causal relations, we generate event graphs from text based on dependencies, and rank answers by aligning event graphs. In particular, the alignments are constrained by graph-based reasoning to ensure temporal and causal agreement. Our focused approach self-adaptively complements existing solutions; it is automatically triggered only when applicable. Experiments on RACE and MCTest show that state-of-the-art methods are notably improved by using our approach as an add-on.
Face Recognition System Based on Principal Component Analysis ( PCA ) with Back Propagation Neural Networks ( BPNN )

Face recognition has received substantial attention from researches in biometrics, pattern recognition field and computer vision communities. Face recognition can be applied in Security measure at Air ports, Passport verification, Criminals list verification in police department, Visa processing , Verification of Electoral identification and Card Security measure at ATM’s. In this paper, a face recognition system for personal identification and verification using Principal Component Analysis (PCA) with Back Propagation Neural Networks (BPNN) is proposed. This system consists on three basic steps which are automatically detect human face image using BPNN, the various facial features extraction, and face recognition are performed based on Principal Component Analysis (PCA) with BPNN. The dimensionality of face image is reduced by the PCA and the recognition is done by the BPNN for efficient and robust face recognition. In this paper also focuses on the face database with different sources of variations, especially Pose, Expression, Accessories, Lighting and backgrounds would be used to advance the state-of-the-art face recognition technologies aiming at practical applications
Distributed representations in memory: insights from functional brain imaging.

Forging new memories for facts and events, holding critical details in mind on a moment-to-moment basis, and retrieving knowledge in the service of current goals all depend on a complex interplay between neural ensembles throughout the brain. Over the past decade, researchers have increasingly utilized powerful analytical tools (e.g., multivoxel pattern analysis) to decode the information represented within distributed functional magnetic resonance imaging activity patterns. In this review, we discuss how these methods can sensitively index neural representations of perceptual and semantic content and how leverage on the engagement of distributed representations provides unique insights into distinct aspects of memory-guided behavior. We emphasize that, in addition to characterizing the contents of memories, analyses of distributed patterns shed light on the processes that influence how information is encoded, maintained, or retrieved, and thus inform memory theory. We conclude by highlighting open questions about memory that can be addressed through distributed pattern analyses.
Dolphin swarm algorithm

By adopting the distributed problem-solving strategy, swarm intelligence algorithms have been successfully applied to many optimization problems that are difficult to deal with using traditional methods. At present, there are many well-implemented algorithms, such as particle swarm optimization, genetic algorithm, artificial bee colony algorithm, and ant colony optimization. These algorithms have already shown favorable performances. However, with the objects becoming increasingly complex, it is becoming gradually more difficult for these algorithms to meet human’s demand in terms of accuracy and time. Designing a new algorithm to seek better solutions for optimization problems is becoming increasingly essential. Dolphins have many noteworthy biological characteristics and living habits such as echolocation, information exchanges, cooperation, and division of labor. Combining these biological characteristics and living habits with swarm intelligence and bringing them into optimization problems, we propose a brand new algorithm named the ‘dolphin swarm algorithm’ in this paper. We also provide the definitions of the algorithm and specific descriptions of the four pivotal phases in the algorithm, which are the search phase, call phase, reception phase, and predation phase. Ten benchmark functions with different properties are tested using the dolphin swarm algorithm, particle swarm optimization, genetic algorithm, and artificial bee colony algorithm. The convergence rates and benchmark function results of these four algorithms are compared to testify the effect of the dolphin swarm algorithm. The results show that in most cases, the dolphin swarm algorithm performs better. The dolphin swarm algorithm possesses some great features, such as first-slow-then-fast convergence, periodic convergence, local-optimum-free, and no specific demand on benchmark functions. Moreover, the dolphin swarm algorithm is particularly appropriate to optimization problems, with more calls of fitness functions and fewer individuals.