nils-herrmann/clean_scirepeval huggingface dataset:
Performance Improvement of Cluster-Based Routing Protocol in VANET

Vehicular ad-hoc NETworks (VANETs) have received considerable attention in recent years, due to its unique characteristics, which are different from mobile ad-hoc NETworks, such as rapid topology change, frequent link failure, and high vehicle mobility. The main drawback of VANETs network is the network instability, which yields to reduce the network efficiency. In this paper, we propose three algorithms: cluster-based life-time routing (CBLTR) protocol, Intersection dynamic VANET routing (IDVR) protocol, and control overhead reduction algorithm (CORA). The CBLTR protocol aims to increase the route stability and average throughput in a bidirectional segment scenario. The cluster heads (CHs) are selected based on maximum lifetime among all vehicles that are located within each cluster. The IDVR protocol aims to increase the route stability and average throughput, and to reduce end-to-end delay in a grid topology. The elected intersection CH receives a set of candidate shortest routes (SCSR) closed to the desired destination from the software defined network. The IDVR protocol selects the optimal route based on its current location, destination location, and the maximum of the minimum average throughput of SCSR. Finally, the CORA algorithm aims to reduce the control overhead messages in the clusters by developing a new mechanism to calculate the optimal numbers of the control overhead messages between the cluster members and the CH. We used SUMO traffic generator simulators and MATLAB to evaluate the performance of our proposed protocols. These protocols significantly outperform many protocols mentioned in the literature, in terms of many parameters.
Wikidata Vandalism Detection - The Loganberry Vandalism Detector at WSDM Cup 2017

Wikidata is the new, large-scale knowledge base of the Wikimedia Foundation. As it can be edited by anyone, entries frequently get vandalized, leading to the possibility that it might spread of falsified information if such posts are not detected. The WSDM 2017 Wiki Vandalism Detection Challenge requires us to solve this problem by computing a vandalism score denoting the likelihood that a revision corresponds to an act of vandalism and performance is measured using the ROC-AUC obtained on a held-out test set. This paper provides the details of our submission that obtained an ROC-AUC score of 0.91976 in the final evaluation.
Blurred image region detection and classification

Many digital images contain blurred regions which are caused by motion or defocus. Automatic detection and classification of blurred image regions are very important for different multimedia analyzing tasks. This paper presents a simple and effective automatic image blurred region detection and classification technique. In the proposed technique, blurred image regions are first detected by examining singular value information for each image pixels. The blur types (i.e. motion blur or defocus blur) are then determined based on certain alpha channel constraint that requires neither image deblurring nor blur kernel estimation. Extensive experiments have been conducted over a dataset that consists of 200 blurred image regions and 200 image regions with no blur that are extracted from 100 digital images. Experimental results show that the proposed technique detects and classifies the two types of image blurs accurately. The proposed technique can be used in many different multimedia analysis applications such as image segmentation, depth estimation and information retrieval.
Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades

Creating datasets for Neuromorphic Vision is a challenging task. A lack of available recordings from Neuromorphic Vision sensors means that data must typically be recorded specifically for dataset creation rather than collecting and labeling existing data. The task is further complicated by a desire to simultaneously provide traditional frame-based recordings to allow for direct comparison with traditional Computer Vision algorithms. Here we propose a method for converting existing Computer Vision static image datasets into Neuromorphic Vision datasets using an actuated pan-tilt camera platform. Moving the sensor rather than the scene or image is a more biologically realistic approach to sensing and eliminates timing artifacts introduced by monitor updates when simulating motion on a computer monitor. We present conversion of two popular image datasets (MNIST and Caltech101) which have played important roles in the development of Computer Vision, and we provide performance metrics on these datasets using spike-based recognition algorithms. This work contributes datasets for future use in the field, as well as results from spike-based algorithms against which future works can compare. Furthermore, by converting datasets already popular in Computer Vision, we enable more direct comparison with frame-based approaches.
The Seduction Script: Psychological and Cultural Norms of Interpersonal Approaches As Markers for Sexual Aggression and Abuse

Hardly any subjects enjoy greater - public or private - interest than the art of flirtation and seduction. However, interpersonal approach behavior not only paves the way for sexual interaction and reproduction, but it simultaneously integrates non-sexual psychobiological and cultural standards regarding consensus and social norms. In the present paper, we use script theory, a concept that extends across psychological and cultural science, to assess behavioral options during interpersonal approaches. Specifically, we argue that approaches follow scripted event sequences that entail ambivalence as an essential communicative element. On the one hand, ambivalence may facilitate interpersonal approaches by maintaining and provoking situational uncertainty, so that the outcome of an action - even after several approaches and dates - remains ambiguous. On the other hand, ambivalence may increase the risk for sexual aggression or abuse, depending on the individual's abilities, the circumstances, and the intentions of the interacting partners. Recognizing latent sequences of sexually aggressive behavior, in terms of their rigid structure and behavioral options, may thus enable individuals to use resources efficiently, avoid danger, and extricate themselves from assault situations. We conclude that interdisciplinary script knowledge about ambivalence as a core component of the seduction script may be helpful for counteracting subtly aggressive intentions and preventing sexual abuse. We discuss this with regard to the nature-nurture debate as well as phylogenetic and ontogenetic aspects of interpersonal approach behavior and its medial implementation.
Comparing Supervised vs . Unsupervised Image Segmentation Methods

This project compares the supervised logistic regression segmentation algorithm against the unsupervised k-means clustering segmentation. We observed that the difference between either method is not very significant. When performed on the 100 test cases for BSD300, the supervised method on average achieved a precision rate of 0.47 and the unsupervised method achieved a precision rate of 0.41. The variance in performance for both method is quite high, indicating that the models performed well for some images but not for other. This effect seems more prevalent for the supervised result. The standard deviation values for the precision rate are σsupervised = 0.18, σunsupervised = 0.16
Idiopathic Small Fiber Neuropathy: Phenotype, Etiologies, and the Search for Fabry Disease

The etiology of small fiber neuropathy (SFN) often remains unclear. Since SFN may be the only symptom of late-onset Fabry disease, it may be underdiagnosed in patients with idiopathic polyneuropathy. We aimed to uncover the etiological causes of seemingly idiopathic SFN by applying a focused investigatory procedure, to describe the clinical phenotype of true idiopathic SFN, and to elucidate the possible prevalence of late-onset Fabry disease in these patients.



Forty-seven adults younger than 60 years with seemingly idiopathic pure or predominantly small fiber sensory neuropathy underwent a standardized focused etiological and clinical investigation. The patients deemed to have true idiopathic SFN underwent genetic analysis of the alpha-galactosidase A gene (GLA) that encodes the enzyme alpha-galactosidase A (Fabry disease).



The following etiologies were identified in 12 patients: impaired glucose tolerance (58.3%), diabetes mellitus (16.6%), alcohol abuse (8.3%), mitochondrial disease (8.3%), and hereditary neuropathy (8.3%). Genetic alterations of unknown clinical significance in GLA were detected in 6 of the 29 patients with true idiopathic SFN, but this rate did not differ significantly from that in healthy controls (n=203). None of the patients with genetic alterations in GLA had significant biochemical abnormalities simultaneously in blood, urine, and skin tissue.



A focused investigation may aid in uncovering further etiological factors in patients with seemingly idiopathic SFN, such as impaired glucose tolerance. However, idiopathic SFN in young to middle-aged Swedish patients does not seem to be due to late-onset Fabry disease.
CLUE: Clustering for Mining Web URLs

The Internet has witnessed the proliferation of applications and services that rely on HTTP as application protocol. Users play games, read emails, watch videos, chat and access web pages using their PC, which in turn downloads tens or hundreds of URLs to fetch all the objects needed to display the requested content. As result, billions of URLs are observed in the network. When monitoring the traffic, thus, it is becoming more and more important to have methodologies and tools that allow one to dig into this data and extract useful information. In this paper, we present CLUE, Clustering for URL Exploration, a methodology that leverages clustering algorithms, i.e., unsupervised techniques developed in the data mining field to extract knowledge from passive observation of URLs carried by the network. This is a challenging problem given the unstructured format of URLs, which, being strings, call for specialized approaches. Inspired by text-mining algorithms, we introduce the concept of URL-distance and use it to compose clusters of URLs using the well-known DBSCAN algorithm. Experiments on actual datasets show encouraging results. Well-separated and consistent clusters emerge and allow us to identify, e.g., malicious traffic, advertising services, and thirdparty tracking systems. In a nutshell, our clustering algorithm offers the means to get insights on the data carried by the network, with applications in the security or privacy protection fields.
Clinical and Functional Outcomes After 2 Years in the Early Detection and Intervention for the Prevention of Psychosis Multisite Effectiveness Trial

To test effectiveness of the Early Detection, Intervention, and Prevention of Psychosis Program in preventing the onset of severe psychosis and improving functioning in a national sample of at-risk youth.



In a risk-based allocation study design, 337 youth (age 12-25) at risk of psychosis were assigned to treatment groups based on severity of positive symptoms. Those at clinically higher risk (CHR) or having an early first episode of psychosis (EFEP) were assigned to receive Family-aided Assertive Community Treatment (FACT); those at clinically lower risk (CLR) were assigned to receive community care. Between-groups differences on outcome variables were adjusted statistically according to regression-discontinuity procedures and evaluated using the Global Test Procedure that combined all symptom and functional measures.



A total of 337 young people (mean age: 16.6) were assigned to the treatment group (CHR + EFEP, n = 250) or comparison group (CLR, n = 87). On the primary variable, positive symptoms, after 2 years FACT, were superior to community care (2 df, p < .0001) for both CHR (p = .0034) and EFEP (p < .0001) subgroups. Rates of conversion (6.3% CHR vs 2.3% CLR) and first negative event (25% CHR vs 22% CLR) were low but did not differ. FACT was superior in the Global Test (p = .0007; p = .024 for CHR and p = .0002 for EFEP, vs CLR) and in improvement in participation in work and school (p = .025).



FACT is effective in improving positive, negative, disorganized and general symptoms, Global Assessment of Functioning, work and school participation and global outcome in youth at risk for, or experiencing very early, psychosis.
The Integrated Behavioural Model for Water, Sanitation, and Hygiene: a systematic review of behavioural models and a framework for designing and evaluating behaviour change interventions in infrastructure-restricted settings

Promotion and provision of low-cost technologies that enable improved water, sanitation, and hygiene (WASH) practices are seen as viable solutions for reducing high rates of morbidity and mortality due to enteric illnesses in low-income countries. A number of theoretical models, explanatory frameworks, and decision-making models have emerged which attempt to guide behaviour change interventions related to WASH. The design and evaluation of such interventions would benefit from a synthesis of this body of theory informing WASH behaviour change and maintenance.



We completed a systematic review of existing models and frameworks through a search of related articles available in PubMed and in the grey literature. Information on the organization of behavioural determinants was extracted from the references that fulfilled the selection criteria and synthesized. Results from this synthesis were combined with other relevant literature, and from feedback through concurrent formative and pilot research conducted in the context of two cluster-randomized trials on the efficacy of WASH behaviour change interventions to inform the development of a framework to guide the development and evaluation of WASH interventions: the Integrated Behavioural Model for Water, Sanitation, and Hygiene (IBM-WASH).



We identified 15 WASH-specific theoretical models, behaviour change frameworks, or programmatic models, of which 9 addressed our review questions. Existing models under-represented the potential role of technology in influencing behavioural outcomes, focused on individual-level behavioural determinants, and had largely ignored the role of the physical and natural environment. IBM-WASH attempts to correct this by acknowledging three dimensions (Contextual Factors, Psychosocial Factors, and Technology Factors) that operate on five-levels (structural, community, household, individual, and habitual).



A number of WASH-specific models and frameworks exist, yet with some limitations. The IBM-WASH model aims to provide both a conceptual and practical tool for improving our understanding and evaluation of the multi-level multi-dimensional factors that influence water, sanitation, and hygiene practices in infrastructure-constrained settings. We outline future applications of our proposed model as well as future research priorities needed to advance our understanding of the sustained adoption of water, sanitation, and hygiene technologies and practices.
Psychosocial Risks: can their effects on the Security of Information Systems really be ignored?

Psychosocial risks at the workplace is a well-researched subject from a managerial and organisational point of view. However, the relation of psychosocial risks to Information Security has not been formally studied to the extent required by the gravity of the topic. An attempt is made to highlight the nature of psychosocial risks and provide examples of their effects on Information Security. The foundation is thus set for methodologies of assessment and mitigation and suggestions are made on future research directions.
Relationship of biofilm formation and different virulence genes in uropathogenic Escherichia coli isolates from Northwest Iran

The Escherichia coli (E. coli) bacterium is one of the main causative agents of urinary tract infections (UTI) worldwide. The ability of this bacterium to form biofilms on medical devices such as catheters plays an important role in the development of UTI. The aim of the present study was to investigate the possible relationship between virulence factors and biofilm formation of E. coli isolates responsible for urinary tract infection.



A total of 100 E. coli isolates isolated from patients with UTI were collected and characterized by routine bacteriological methods. In vitro biofilm formation by these isolates was determined using the 96-well microtiter-plate test, and the presence of fimA, papC, and hly virulence genes was examined by PCR assay. Data analysis was performed using SPSS 16.0 software.



From 100 E. coli isolates isolated from UTIs, 92% were shown to be biofilm positive. The genes papC, fimA, and hly were detected in 43%, 94% and 26% of isolates, respectively. Biofilm formation in isolates that expressed papC, fimA, and hly genes was 100%, 93%, and 100%, respectively. A significant relationship was found between presence of the papC gene and biofilm formation in E. coli isolates isolated from UTI (P<0.01), but there was no statistically significant correlation between presence of fimA and hly genes with biofilm formation (P<0.072, P<0.104).



RESULTS showed that fimA and hly genes do not seem to be necessary or sufficient for the production of biofilm in E. coli, but the presence of papC correlates with increased biofilm formation of urinary tract isolates. Overall, the presence of fimA, papC, and hly virulence genes coincides with in vitro biofilm formation in uropathogenic E. coli isolates.
Promoting interprofessional education.

The work of the Institute of Medicine and others has clearly demonstrated that when healthcare professionals understand each others' roles and are able to communicate and work effectively together, patients are more likely to receive safe, quality care. Currently, there are few opportunities to bring faculty and students in pre-licensure programs from multiple disciplines together for the purpose of learning together about each others' roles, and practicing collaboration and teamwork. Designing and implementing interprofessional education offerings is challenging. Course scheduling, faculty interest and expertise in interprofessional education (IPE), a culture of IPE among faculty and students, and institutional policies for sharing course credit among schools are just a few of the challenges. This article explores the concept of IPE, and how faculty in schools of nursing might take the lead to work with colleagues in other health profession schools to prepare graduates to understand each others' roles, and the importance of teamwork, communication, and collaboration to the delivery of high quality, safe patient care.
Fraud detection in big data using supervised and semi-supervised learning techniques

This paper addresses the credit card fraud detection problem in the context of Big Data, based on machine learning techniques. In the fraud detection task, typically the available datasets for ML training present some peculiarities, such as the unavoidable condition of a strong class imbalance, the existence of unlabeled transactions, and the large number of records that must be processed. The present paper aims to propose a methodology for automatic detection of fraudulent transactions, that tackle all these problems. The methodology is based on a Balanced Random Forest, that can be used in supervised and semi-supervised scenarios through a co-training approach. Two different schemes for the co-training approach are tested, in order to overcome the class imbalance problem. Moreover, a Spark platform and Hadoop file system support our solution, in order to enable the scalability of the proposed solution. The proposed approach achieves an absolute improvement of around 24% in terms of geometric mean in comparison to a standard random forest learning strategy.
Metasurface holograms for visible light

Holography, a revolutionary 3D imaging technique, has been developed for storing and recovering the amplitude and phase of light scattered by objects. Later, single-beam computer-generated phase holography was proposed for restoring the wavefront from a given incidence. However, because the phase modulation depends on the light propagation inside the material, the thickness of phase holograms usually remains comparable to the wavelength. Here we experimentally demonstrate ultra-thin metasurface holograms that operate in the visible range whose thickness is only 30 nm (approximately 1/23 of the operational wavelength). To our knowledge, this is the thinnest hologram that can provide both amplitude and phase modulation in the visible wavelength range, which generates highresolution low-noise images. Using this technique, not only the phase, but potentially the amplitude of the incident wave can be efficiently controlled, expanding the route to new applications of ultra-thin and surface-confined photonic devices.
Towards Exploratory OLAP Over Linked Open Data - A Case Study

Business Intelligence (BI) tools provide fundamental support for analyzing large volumes of information. Data Warehouses (DW) and Online Analytical Processing (OLAP) tools are used to store and analyze data. Nowadays more and more information is available on the Web in the form of Resource Description Framework (RDF), and BI tools have a huge potential of achieving better results by integrating real-time data from web sources into the analysis process. In this paper, we describe a framework for so-called exploratory OLAP over RDF sources. We propose a system that uses a multidimensional schema of the OLAP cube expressed in RDF vocabularies. Based on this information the system is able to query data sources, extract and aggregate data, and build a cube. We also propose a computer-aided process for discovering previously unknown data sources and building a multidimensional schema of the cube. We present a use case to demonstrate the applicability of the approach.
Multivariate volumetric data analysis and visualization through bottom-up subspace exploration

Multivariate volumetric datasets are often encountered in results generated by scientific simulations. Compared to univariate datasets, analysis and visualization of multivariate datasets are much more challenging due to the complex relationships among the variables. As an effective way to visualize and analyze multivariate datasets, volume rendering has been frequently used, although designing good multivariate transfer functions is still non-trivial. In this paper, we present an interactive workflow to allow users to design multivariate transfer functions. To handle large scale datasets, in the preprocessing stage we reduce the number of data points through data binning and aggregation, and then a new set of data points with a much smaller size are generated. The relationship between all pairs of variables is presented in a matrix juxtaposition view, where users can navigate through the different subspaces. An entropy based method is used to help users to choose which subspace to explore. We proposed two weights: scatter weight and size weight that are associated with each projected point in those different subspaces. Based on those two weights, data point filter and kernel density estimation operations are employed to assist users to discover interesting features. For each user-selected feature, a Gaussian function is constructed and updated incrementally. Finally, all those selected features are visualized through multivariate volume rendering to reveal the structure of data. With our system, users can interactively explore different subspaces and specify multivariate transfer functions in an effective way. We demonstrate the effectiveness of our system with several multivariate volumetric datasets.