nils-herrmann/clean_scirepeval huggingface dataset:
Web-based personalized hybrid book recommendation system

Recommender Systems have been around for more than a decade now. Choosing what book to read next has always been a question for many. Even for students, deciding which textbook or reference book to read on a topic unknown to them is a big question. In this paper, we try to present a model for a web-based personalized hybrid book recommender system which exploits varied aspects of giving recommendations apart from the regular collaborative and content-based filtering approaches. Temporal aspects for the recommendations are incorporated. Also for users of different age, gender and country, personalized recommendations can be made on these demographic parameters. Scraping information from the web and using the information obtained from this process can be equally useful in making recommendations.
Stress, burnout and health in the clinical period of dental education.

The study examined the extent of stress, burnout and health problems experienced by fourth and fifth year dental students from the three universities of Dresden, Freiburg and Bern. The objectives of the study were to: (i) identify frequent sources of stress and to report the prevalence rates of burnout and health problems in dental students, (ii) determine the rate of students suffering from severe burnout symptoms and (iii) identify stress factors related to the burnout symptoms of emotional exhaustion and depersonalization. A total of 161 dental students from Dresden, Freiburg and Bern participated in the study. They completed the Psychosocial Stress Inventory, the Maslach Burnout Inventory and the Health Survey Questionnaire. Frequent sources of stress were limitation of leisure time, examination anxiety and the transition stress that was related to the adaptation to the demands of the clinical phase of dental education. Few differences existed between the students of the fourth and the fifth study year. Study-related stress was lowest in Bern and considerably higher in Dresden. Differences of mean levels of burnout symptoms were found only for the burnout dimension of emotional exhaustion. Students from Dresden and Freiburg were more emotionally exhausted than students from Bern, students from Dresden also reported more health problems than students from Bern or Freiburg. Ten per cent of the dental students suffered from severe emotional exhaustion, 17% complained about a severe lack of accomplishment and 28% reported severe depersonalization symptoms. Forty-four per cent of the variance of emotional exhaustion was explained by study-related factors such as lack of leisure time, examination anxiety and transition stress. The only predictor of depersonalization was a lack of social integration, accounting for 3% of the variance. A lack of social integration may be an indicator of low social competence which may cause difficulties in dealing with patients adequately and therefore result in depersonalization. The results indicate a need to identify the group of students who may have insufficient social skills for dealing adequately with the patients, and to train them accordingly.
Microstrip Branch-Line Couplers for Crossover Application

The branch-line coupler may be redesigned for crossover application. The bandwidth of such a coupler can be extended by suitably incorporating additional sections into the composite design. Laboratory tests on microstrip prototypes have shown the return loss and isolation of the three- and four-section couplers to be better than 20 dB over bandwidths of 22% and 33%, respectively. The insertion losses and group delays vary by less than ±0.05 dB and ±1 ns, respectively, for both prototypes.
Design of a Clutch – Spring Knee Exoskeleton for Running

Because the leg is known to exhibit springlike behavior during the stance phase of running, several exoskeletons have attempted to place external springs in parallel with some or all of the leg during stance, but these designs have failed to permit natural kinematics during swing. To this end, a parallel-elastic exoskeleton is presented that introduces a clutch to disengage the parallel leg-spring and thereby not constrain swing-phase movements of the biological leg. A custom interference clutch with integrated planetary gear transmission, made necessary by the requirement for high holding torque but low mass, is presented and shown to withstand up to 190 N m at 1.8 deg resolution with a mass of only 710 g. A suitable control strategy for locking the clutch at peak knee extension is also presented, where only an onboard rate gyroscope and exoskeletal joint encoder are employed as sensory inputs. Exoskeletal electromechanics, sensing, and control are shown to achieve design critieria necessary to emulate biological knee stiffness behaviors in running.
Democracy online: civility, politeness, and the democratic potential of online political discussion groups

The proponents of cyberspace promise that online discourse will increase political participation and pave the road for a democratic utopia. This article explores the potential for civil discourse in cyberspace by examining the level of civility in 287 discussion threads in political newsgroups. While scholars often use civility and politeness interchangeably, this study argues that this conflation ignores the democratic merit of robust and heated discussion. Therefore, civility was defined in a broader sense, by identifying as civil behaviors that enhance democratic conversation. In support of this distinction, the study results revealed that most messages posted on political newsgroups were civil, and further suggested that because the absence of face-to-face communication fostered more heated discussion, cyberspace might actually promote Lyotard’s vision of democratic emancipation through disagreement and anarchy (Lyotard, 1984). Thus, this study supported the internet’s potential to revive the public sphere, provided that greater diversity and volume of discussion is present.
Exploiting Dynamic Resource Allocation for Efficient Parallel Data Processing in the Cloud

In recent years ad hoc parallel data processing has emerged to be one of the killer applications for Infrastructure-as-a-Service (IaaS) clouds. Major Cloud computing companies have started to integrate frameworks for parallel data processing in their product portfolio, making it easy for customers to access these services and to deploy their programs. However, the processing frameworks which are currently used have been designed for static, homogeneous cluster setups and disregard the particular nature of a cloud. Consequently, the allocated compute resources may be inadequate for big parts of the submitted job and unnecessarily increase processing time and cost. In this paper, we discuss the opportunities and challenges for efficient parallel data processing in clouds and present our research project Nephele. Nephele is the first data processing framework to explicitly exploit the dynamic resource allocation offered by today's IaaS clouds for both, task scheduling and execution. Particular tasks of a processing job can be assigned to different types of virtual machines which are automatically instantiated and terminated during the job execution. Based on this new framework, we perform extended evaluations of MapReduce-inspired processing jobs on an IaaS cloud system and compare the results to the popular data processing framework Hadoop.
Distributed internal anomaly detection system for Internet-of-Things

We present overview of a distributed internal anomaly detection system for Internet-of-things. In the detection system, each node monitors its neighbors and if abnormal behavior is detected, the monitoring node will block the packets from the abnormally behaving node at the data link layer and reports to its parent node. The reporting propagates from child to parent nodes until it reaches the root. A novel control message, distress propagation object (DPO), is devised to report the anomaly to the subsequent parents and ultimately the edge-router. The DPO message is integrated to routing protocol for low-power and lossy networks (RPL). The system has configurable profile settings and it is able to learn and differentiate the nodes' normal and suspicious activities without a need for prior knowledge. It has different subsystems and operation phases at data link and network layers, which share a common repository in a node. The system uses network fingerprinting to be aware of changes in network topology and nodes' positions without any assistance from a positioning system.
Mining multilingual topics from wikipedia

In this paper, we try to leverage a large-scale and multilingual knowledge base, Wikipedia, to help effectively analyze and organize Web information written in different languages. Based on the observation that one Wikipedia concept may be described by articles in different languages, we adapt existing topic modeling algorithm for mining multilingual topics from this knowledge base. The extracted 'universal' topics have multiple types of representations, with each type corresponding to one language. Accordingly, new documents of different languages can be represented in a space using a group of universal topics, which makes various multilingual Web applications feasible.
Personal authentication using palm-print features

Biometrics-based authentication is a veri,cation approach using the biological features inherent in each individual. They are processed based on the identical, portable, and arduous duplicate characteristics. In this paper, we propose a scanner-based personal authentication system by using the palm-print features. It is very suitable in many network-based applications. The authentication system consists of enrollment and veri,cation stages. In the enrollment stage, the training samples are collected and processed by the pre-processing, feature extraction, and modeling modules to generate the matching templates. In the veri,cation stage, a query sample is also processed by the pre-processing and feature extraction modules, and then is matched with the reference templates to decide whether it is a genuine sample or not. The region of interest (ROI) for each sample is ,rst obtained from the pre-processing module. Then, the palm-print features are extracted from the ROI by using Sobel and morphological operations. The reference templates for a speci,c user are generated in the modeling module. Last, we use the template-matching and the backpropagation neural network to measure the similarity in the veri,cation stage. Experimental results verify the validity of our proposed approaches in personal authentication.? 2002 Pattern Recognition Society.
Social Media: A Review and Tutorial of Applications in Medicine and Health Care

Social media are dynamic and interactive computer-mediated communication tools that have high penetration rates in the general population in high-income and middle-income countries. However, in medicine and health care, a large number of stakeholders (eg, clinicians, administrators, professional colleges, academic institutions, ministries of health, among others) are unaware of social media's relevance, potential applications in their day-to-day activities, as well as the inherent risks and how these may be attenuated and mitigated.



We conducted a narrative review with the aim to present case studies that illustrate how, where, and why social media are being used in the medical and health care sectors.



Using a critical-interpretivist framework, we used qualitative methods to synthesize the impact and illustrate, explain, and provide contextual knowledge of the applications and potential implementations of social media in medicine and health care. Both traditional (eg, peer-reviewed) and nontraditional (eg, policies, case studies, and social media content) sources were used, in addition to an environmental scan (using Google and Bing Web searches) of resources.



We reviewed, evaluated, and synthesized 76 articles, 44 websites, and 11 policies/reports. Results and case studies are presented according to 10 different categories of social media: (1) blogs (eg, WordPress), (2) microblogs (eg, Twitter), (3) social networking sites (eg, Facebook), (4) professional networking sites (eg, LinkedIn, Sermo), (5) thematic networking sites (eg, 23andMe), (6) wikis (eg, Wikipedia), (7) mashups (eg, HealthMap), (8) collaborative filtering sites (eg, Digg), (9) media sharing sites (eg, YouTube, Slideshare), and others (eg, SecondLife). Four recommendations are provided and explained for stakeholders wishing to engage with social media while attenuating risk: (1) maintain professionalism at all times, (2) be authentic, have fun, and do not be afraid, (3) ask for help, and (4) focus, grab attention, and engage.



The role of social media in the medical and health care sectors is far reaching, and many questions in terms of governance, ethics, professionalism, privacy, confidentiality, and information quality remain unanswered. By following the guidelines presented, professionals have a starting point to engage with social media in a safe and ethical manner. Future research will be required to understand the synergies between social media and evidence-based practice, as well as develop institutional policies that benefit patients, clinicians, public health practitioners, and industry alike.
Feature Extraction and Classification of EEG Signal Using Neural Network Based Techniques

Feature extraction of EEG signals is core issues on EEG based brain mapping analysis. The classification of EEG signals has been performed using features extracted from EEG signals. Many features have proved to be unique enough to use in all brain related medical application. EEG signals can be classified using a set of features like Autoregression, Energy Spectrum Density, Energy Entropy, and Linear Complexity. However, different features show different discriminative power for different subjects or different trials. In this research, two-features are used to improve the performance of EEG signals. Neural Network based techniques are applied to feature extraction of EEG signal. This paper discuss on extracting features based on Average method and Max & Min method of the data set. The Extracted Features are classified using Neural Network Temporal Pattern Recognition Technique. The two methods are compared and performance is analyzed based on the results obtained from the Neural Network classifier.
Learning to Detect Video Saliency With HEVC Features

Saliency detection has been widely studied to predict human fixations, with various applications in computer vision and image processing. For saliency detection, we argue in this paper that the state-of-the-art High Efficiency Video Coding (HEVC) standard can be used to generate the useful features in compressed domain. Therefore, this paper proposes to learn the video saliency model, with regard to HEVC features. First, we establish an eye tracking database for video saliency detection, which can be downloaded from https://github.com/remega/video_database. Through the statistical analysis on our eye tracking database, we find out that human fixations tend to fall into the regions with large-valued HEVC features on splitting depth, bit allocation, and motion vector (MV). In addition, three observations are obtained with the further analysis on our eye tracking database. Accordingly, several features in HEVC domain are proposed on the basis of splitting depth, bit allocation, and MV. Next, a kind of support vector machine is learned to integrate those HEVC features together, for video saliency detection. Since almost all video data are stored in the compressed form, our method is able to avoid both the computational cost on decoding and the storage cost on raw data. More importantly, experimental results show that the proposed method is superior to other state-of-the-art saliency detection methods, either in compressed or uncompressed domain.
Fast Prediction for Sparse Time Series: Demand Forecast of EV Charging Stations for Cell Phone Applications

This paper proposes a new cellphone application algorithm which has been implemented for the prediction of energy consumption at electric vehicle (EV) charging stations at the University of California, Los Angeles (UCLA). For this interactive user application, the total time for accessing the database, processing the data, and making the prediction needs to be within a few seconds. We first analyze three relatively fast machine learning-based time series prediction algorithms and find that the nearest neighbor (NN) algorithm (k NN with k = 1) shows better accuracy. Considering the sparseness of the time series of the charging records, we then discuss the new algorithm based on the new proposed time-weighted dot product (TWDP) dissimilarity measure to improve the accuracy and processing time. Two applications have been designed on top of the proposed prediction algorithm: one predicts the expected available energy at the outlet and the other one predicts the expected charging finishing time. The total time, including accessing the database, data processing, and prediction is approximately 1 s for both applications. The granularity of the prediction is 1 h and the horizon is 24 h; data have been collected from 20 EV charging outlets.
Smartphone-based crosswalk detection and localization for visually impaired pedestrians

This paper describes recent work on the “Crosswatch” project, which is a computer vision-based smartphone system developed for providing guidance to blind and visually impaired travelers at traffic intersections. A key function of Crosswatch is self-localization - the estimation of the user's location relative to the crosswalks in the current traffic intersection. Such information may be vital to users with low or no vision to ensure that they know which crosswalk they are about to enter, and are properly aligned and positioned relative to the crosswalk. However, while computer vision-based methods have been used for finding crosswalks and helping blind travelers align themselves to them, these methods assume that the entire crosswalk pattern can be imaged in a single frame of video, which poses a significant challenge for a user who lacks enough vision to know where to point the camera so as to properly frame the crosswalk. In this paper we describe work in progress that tackles the problem of crosswalk detection and self-localization, building on recent work describing techniques enabling blind and visually impaired users to acquire 360° image panoramas while turning in place on a sidewalk. The image panorama is converted to an aerial (overhead) view of the nearby intersection, centered on the location that the user is standing at, so as to facilitate matching with a template of the intersection obtained from Google Maps satellite imagery. The matching process allows crosswalk features to be detected and permits the estimation of the user's precise location relative to the crosswalk of interest. We demonstrate our approach on intersection imagery acquired by blind users, thereby establishing the feasibility of the approach.
Human RCS measurements and dummy requirements for the assessment of radar based active pedestrian safety systems

Based on EuroNCAP regulations the number of autonomous emergency braking systems for pedestrians (AEB-P) will increase over the next years. According to accident research a considerable amount of severe pedestrian accidents happen at artificial lighting, twilight or total darkness conditions. Because radar sensors are very robust in these situations, they will play an important role for future AEB-P systems. To assess and evaluate systems a pedestrian dummy with reflection characteristics as close as possible to real humans is indispensable. As an extension to existing measurements in literature this paper addresses open issues like the influence of different positions of the limbs or different clothing for both relevant automotive frequency bands. Additionally suggestions and requirements for specification of pedestrian dummies based on results of RCS measurements of humans and first experimental developed dummies are given.
Recovering from Random Pruning: On the Plasticity of Deep Convolutional Neural Networks

Recently there has been a lot of work on pruning filters from deep convolutional neural networks (CNNs) with the intention of reducing computations. The key idea is to rank the filters based on a certain criterion (say, l1-norm, average percentage of zeros, etc) and retain only the top ranked filters. Once the low scoring filters are pruned away the remainder of the network is fine tuned and is shown to give performance comparable to the original unpruned network. In this work, we report experiments which suggest that the comparable performance of the pruned network is not due to the specific criterion chosen but due to the inherent plasticity of deep neural networks which allows them to recover from the loss of pruned filters once the rest of the filters are fine-tuned. Specifically, we show counter-intuitive results wherein by randomly pruning 25-50% filters from deep CNNs we are able to obtain the same performance as obtained by using state of the art pruning methods. We empirically validate our claims by doing an exhaustive evaluation with VGG-16 and ResNet-50. Further, we also evaluate a real world scenario where a CNN trained on all 1000 ImageNet classes needs to be tested on only a small set of classes at test time (say, only animals). We create a new benchmark dataset from ImageNet to evaluate such class specific pruning and show that even here a random pruning strategy gives close to state of the art performance. Lastly, unlike existing approaches which mainly focus on the task of image classification, in this work we also report results on object detection. We show that using a simple random pruning strategy we can achieve significant speed up in object detection (74% improvement in fps) while retaining the same accuracy as that of the original Faster RCNN model.
The Program Dependence Graph in a Software Development Environment

The internal program representation chosen for a software development environment plays a critical role in the nature of that environment. A form should facilitate implementation and contribute to the responsiveness of the environment to the user. The program dependence graph (PDG) may be a suitable internal form. It allows programs to be sliced in linear time for debugging and for use by language-directed editors. The slices obtained are more accurate than those obtained with existing methods because I/O is accounted for correctly and irrelevant statements on multi-statement lines are not displayed. The PDG may be interpreted in a data driven fashion or may have highly optimized (including vectorized) code produced from it. It is amenable to incremental data flow analysis, improving response time to the user in an interactive environment and facilitating debugging through data flow anomaly detection. It may also offer a good basis for software complexity metrics, adding to the completeness of an environment based on it.
Extraction of land use / land cover-related information from very high resolution data in urban and suburban areas

Very High Resolution (VHR) satellite images offer a great potential for the extraction of landuse and land-cover related information for urban areas. The available techniques are diverse and need to be further examined before operational use is possible. In this paper we applied two pixel-by-pixel classification techniques and the object-oriented image analysis approach (eCognition) for a land-cover classification of a Quickbird image of a study area in the northern part of the city of Ghent (Belgium). Only small differences in overall Kappa were noted between the best results of the pixel-based approach (neural network classification with Haralick texture measures) and the object-oriented classification (eCognition). A rule-based procedure using ancillary information on elevation derived from a digital surface model was applied on the pixel-based land-cover classification in order to obtain information on the spatial distribution of buildings and artificial surfaces.