nils-herrmann/clean_scirepeval huggingface dataset:
Internet of Vehicles: Architecture, Protocols, and Security
Venue: IEEE Internet of Things Journal
Year: 2018

Today, vehicles are increasingly being connected to the Internet of Things which enable them to provide ubiquitous access to information to drivers and passengers while on the move. However, as the number of connected vehicles keeps increasing, new requirements (such as seamless, secure, robust, scalable information exchange among vehicles, humans, and roadside infrastructures) of vehicular networks are emerging. In this context, the original concept of vehicular ad hoc networks is being transformed into a new concept called the Internet of Vehicles (IoV) We discuss the benefits of IoV along with recent industry standards developed to promote its implementation. We further present recently proposed communication protocols to enable the seamless integration and operation of the IoV. Finally, we present future research directions of IoV that require further consideration from the vehicular research community.
Bidirectional RNN based Few shot Training for Detecting Multi stage Attack
Venue: Inscrypt
Year: 2020

"Feint Attack" as a new type of APT attack, has become the focus of attention. It adopts a multi stage attacks mode which can be concluded as a combination of virtual attacks and real attacks. Under the cover of virtual attacks, real attacks can achieve the real purpose of the attacker, as a result, it often caused huge losses inadvertently. However, to our knowledge, all previous works use common methods such as Causal Correlation or Cased based to detect outdated multi stage attacks. Few attentions have been paid to detect the "Feint Attack" because the difficulty of detection lies in the diversification of the concept of "Feint Attack" and the lack of professional datasets, many detection methods ignore the semantic relationship in the attack. Aiming at the existing challenge, this paper explores a new method to solve the problem. In the attack scenario, the fuzzy clustering method based on attribute similarity is used to mine multi stage attack chains. Then we use a few shot deep learning algorithm (SMOTE&CNN SVM) and bidirectional Recurrent Neural Network model (Bi RNN) to obtain the "Feint Attack" chains. "Feint Attack" is simulated by the real attack inserted in the normal causal attack chain, and the addition of the real attack destroys the causal relationship of the original attack chain. So we used Bi RNN coding to obtain the hidden feature of "Feint Attack" chain. In the end, our method achieved the goal to detect the "Feint Attack" accurately by using the LLDoS1.0 and LLDoS2.0 of DARPA2000 and CICIDS2017 of Canadian Institute for Cybersecurity.
Reputations at Stake: The German Federal Court's Decision concerning Google's Liability for Autocomplete Suggestions in the International Context
Year: 2013

This article discusses the German Federal Supreme Court decision in Bundesgerichtshof (Google Autocomplete) (VI ZR 269/12) on whether Google was responsible for the publication of results generated by the "autocomplete" function of its search engine that made defamatory suggestions about the claimant. It compares rulings from other jurisdictions on the liability of internet service providers for the publication of defamatory statements.
Bayesian Inference of Linear Temporal Logic Specifications for Contrastive Explanations
Venue: IJCAI
Year: 2019

Temporal logics are useful for providing concise descriptions of system behavior, and have been successfully used as a language for goal definitions in task planning. Prior works on inferring temporal logic specifications have focused on "summarizing" the input dataset i.e. finding specifications that are satisfied by all plan traces belonging to the given set. In this paper, we examine the problem of inferring specifications that describe temporal differences between two sets of plan traces. We formalize the concept of providing such contrastive explanations, then present BayesLTL a Bayesian probabilistic model for inferring contrastive explanations as linear temporal logic (LTL) specifications. We demonstrate the robustness and scalability of our model for inferring accurate specifications from noisy data and across various benchmark planning domains.
Taxonomy of Digital Platforms: A Platform Architecture Perspective
Venue: Wirtschaftsinformatik
Year: 2019

Digital platforms technical core artefacts augmented by peripheral third party derivatives afford organizations to integrate resources in networked business ecosystems. Although digital platforms widely differ in their configurations, digital platforms' dimensions and characteristics to disentangle different digital platform configurations are under researched. To bridge this void, we employ Nickerson et al.'s method for taxonomy development to systematically derive a taxonomy of digital platforms. Specifically, we embrace a platform architecture perspective to capture the configuration of digital platform's components. The resultant taxonomy facilitates a more pronounced understanding and grouping of digital platforms as configurations of certain dimensions and characteristics. Our findings suggest that digital platforms exhibit characteristics on at least four dimensions namely, infrastructure, core, ecosystem, and service dimensions. Second, through instantiating the taxonomy, we find that digital platforms that exhibit similar characteristics share identical architectural profiles and, therefore, belong to one of three digital platform archetypes namely, orchestration, amalgamation, and innovation platforms.
Changing tastes in food media a study of recipe sharing traditions in the food blogging community
Year: 2013

Food is inherently cultural yet traditionally overlooked in many disciplines as a topic worthy of serious investigation. This thesis investigates how food, as a topic of interest, is thriving in an online environment through recipe sharing on food blogs. It applies an ethnographic approach to online community studies, providing a rich description of the food blogging community. The thesis demonstrates how the food blogging can be seen as a community. Through a case study focusing on a one recipe shared across many blogs, it also examines the community in action. As the community has grown, it has become more complex, structured and diverse. The thesis examines its evolution and the response of food related media and other industries to food blogging. The nature of the food blogging community reflects the cultural and social nature of food and the ongoing evolution of recipe sharing through food related media. Food blogs provide an insight into the eating habits of 'ordinary' people, in a more broad based manner than traditional food related media such as cookbooks. Beyond this, food blogs are part of wider cultural trends towards DIY, and provide a useful example of the ongoing transformation of food related media, food culture, and indeed, culture more broadly.
Form birefringence induced in multicomponent glass by femtosecond laser direct writing.
Venue: Optics letters
Year: 2016

We demonstrate a new kind of form birefringence in lithium niobium silicate glass induced by femtosecond laser direct writing. By combining electron backscatter diffraction and transmission electron microscopy, we reveal a self assembled nanostructure consisting of periodic phase change: nonlinear optical nanocrystals embedded in a network of "walls" in a vitreous phase. These "walls" are aligned perpendicular to the laser polarization direction. This self organized nanostructure may successfully explain the origin of the laser induced birefringence in this multicomponent glass quite differently from pure silica. These findings highlight a spectacular modification of glass, and enable construction of a high contrast three dimensional refractive index and birefringent structures at the micrometer scale in multicomponent glasses.
Self management interventions for adults with chronic kidney disease: a scoping review
Venue: BMJ Open
Year: 2018

To systematically identify and describe self management interventions for adult patients with chronic kidney disease (CKD) Setting Community based.  Adults with CKD stages 1 5 (not requiring kidney replacement therapy) Interventions Self management strategies for adults with CKD. Primary and secondary outcome measures Using a scoping review, electronic databases and grey literature were searched in October 2016 to identify self management interventions for adults with CKD stages 1 5 (not requiring kidney replacement therapy) Randomised controlled trials (RCTs) non RCTs, qualitative and mixed method studies were included and study selection and data extraction were independently performed by two reviewers. Outcomes included behaviours, cognitions, physiological measures, symptoms, health status and healthcare.  Fifty studies (19 RCTs, 7 quasi experimental, 5 observational, 13 pre post intervention, 1 mixed method and 5 qualitative) reporting 45 interventions were included. The most common intervention topic was diet/nutrition and interventions were regularly delivered face to face. Interventions were administered by a variety of providers, with nursing professionals the most common health professional group. Cognitions (ie, changes in general CKD knowledge, perceived self management and motivation) were the most frequently reported outcome domain that showed improvement. Less than 1% of the interventions were co developed with patients and 20% were based on a theory or framework.  There was a wide range of self management interventions with considerable variability in outcomes for adults with CKD. Major gaps in the literature include lack of patient engagement in the design of the interventions, with the majority of interventions not applying a behavioural change theory to inform their development. This work highlights the need to involve patients to co developed and evaluate a self management intervention based on sound theories and clinical evidence.
Chaos Theory meets deep learning On Lyapunov exponents and adversarial perturbations
Year: 2018

In this paper, we would like to disseminate a serendipitous discovery involving Lyapunov exponents of a 1 D time series and their use in serving as a filtering defense tool against a specific kind of deep adversarial perturbation. To this end, we use the state of the art CleverHans library to generate adversarial perturbations against a standard Convolutional Neural Network (CNN) architecture trained on the MNIST dataset. We empirically demonstrate how the Lyapunov exponents computed on the flattened 1 D vector representations of the images served as highly discriminative features that could be to pre classify images as adversarial or legitimate before feeding the image into the CNN for classification. 1. Background on defenses against adversarial attacks In the recent past, a plethora of defenses against adversarial attacks have been proposed. These include SafetyNet  adversarial training [19] label smoothing [20] defensive distillation  and feature squeezing [21, 22] to name a few. There is also an ongoing Kaggle contest[2] underway for exploring novel defenses against adversarial attacks. As evinced by the recent spurt in the papers written on this topic, most defenses proposed are quelled by a novel attack that exploits some weakness in the defense. In  the authors queried if one could concoct a strong defense by combining multiple defenses and showed that an ensemble of weak defenses was not sufficient in providing strong defense against adversarial examples that they were able to craft. With this background, we shall now look more closely at a specific type of defense and motivate the relevance of our method within this framework. 1.1. The pre detector based defenses One prominent approach that emerges in the literature of adversarial defenses is that of crafting pre detection and filtering systems that flag inputs that might be potentially adversarial. In  the authors posit that adversarial examples are not drawn from the same distribution as the legitimate samples and can thus be detected using statistical tests. In [14, 7] the authors train a separate binary classifier to first classify any input image as legitimate or adversarial and then perform inference on the passed images. In approaches such as [6] the authors assume that DNNs classify accurately only near the small manifold of training data and that the synthetic adversarial samples do not lie on the data manifold. They apply dropout at test time to ascertain the confidence of adversariality of the input image. In this paper, we would like to disseminate a modelagnostic approach towards adversarial defense that is dependent purely on the quasi time series statistics of the input images that was discovered in a rather serendipitous fashion. The goal is to not present the method we propose as a fool proof adversarial filter, but to instead draw the attention of the DNN and CV communities towards this chance discovery that we feel is worthy of further inquiry.
Safety and Efficacy of the BNT162b2 mRNA Covid 19 Vaccine
Venue: The New England journal of medicine
Year: 2020

Severe acute respiratory syndrome coronavirus 2 (SARS CoV 2) infection and the resulting coronavirus disease 2019 (Covid 19) have afflicted tens of millions of people in a worldwide pandemic. Safe and effective vaccines are needed urgently.  In an ongoing multinational, placebo controlled, observer blinded, pivotal efficacy trial, we randomly assigned persons 16 years of age or older in a 1:1 ratio to receive two doses, 21 days apart, of either placebo or the BNT162b2 vaccine candidate (30 mg per dose) BNT162b2 is a lipid nanoparticle formulated, nucleoside modified RNA vaccine that encodes a prefusion stabilized, membrane anchored SARS CoV 2 full length spike protein. The primary end points were efficacy of the vaccine against laboratory confirmed Covid 19 and safety.  A total of 43,548 participants underwent randomization, of whom 43,448 received injections: 21,720 with BNT162b2 and 21,728 with placebo. There were 8 cases of Covid 19 with onset at least 7 days after the second dose among participants assigned to receive BNT162b2 and 162 cases among those assigned to placebo; BNT162b2 was 95% effective in preventing Covid 19 (95% credible interval, 90.3 to 97.6) Similar vaccine efficacy (generally 90 to 100% was observed across subgroups defined by age, sex, race, ethnicity, baseline body mass index, and the presence of coexisting conditions. Among 10 cases of severe Covid 19 with onset after the first dose, 9 occurred in placebo recipients and 1 in a BNT162b2 recipient. The safety profile of BNT162b2 was characterized by short term, mild to moderate pain at the injection site, fatigue, and headache. The incidence of serious adverse events was low and was similar in the vaccine and placebo groups.  A two dose regimen of BNT162b2 conferred 95% protection against Covid 19 in persons 16 years of age or older. Safety over a median of 2 months was similar to that of other viral vaccines.
Online fluorescence monitoring of RO fouling and integrity: analysis of two contrasting recycled water schemes
Year: 2015

A fluorescence based sensor was used to conduct real time, online monitoring of reverse osmosis (RO) membranes at two Australian municipal advanced water recycling schemes. Trials monitored real time changes in fluorescent organics, measured at lEx/Em 350/430 nm (Peak C) in RO permeates as an indicator of water quality and membrane performance. The RO membranes in each plant were operated using contrasting conditions; the Water Reclamation and Management Scheme plant was operated under constant pressure and the Ground Water Replenishment Trials pilot plant under constant flux. The Peak C sensor detected differences between permeates from staged RO systems, changes in water quality due to an underperforming membrane suspected to have an integrity breach, and changes in water quality from increasing transmembrane pressure in fouled membranes. Peak C fluorescence had stronger correlation with transmembrane pressure and may have potential application in detecting the onset of membrane fouling. Interestingly, while RO EC signals indicated improving permeate quality, fluorescence revealed an increase in (Peak C) organics in the RO permeate. This demonstrated that the passage of fluorescent organics and ionic species are dissimilar, and monitoring EC alone is not an accurate surrogate measure to ensure on going effective removal of organic chemicals.
A multi objective ensemble method for online class imbalance learning
Venue: 2014 International Joint Conference on Neural Networks (IJCNN)
Year: 2014

Online class imbalance learning is an emerging learning area that combines the challenges of both online learning and class imbalance learning. In addition to the learning difficulty from the imbalanced distribution, another major challenge is that the imbalanced rate in a data stream can be dynamically changing. OOB and UOB are two state of the art methods for online class imbalance problems  UOB is better at recognizing minority class examples when the imbalance rate does not change much over time, while OOB is more prepared for the case with a dynamic rate. Aiming for an effective method for both static and dynamic cases, this paper proposes a multi objective ensemble method MOSOB that combines OOB and UOB. MOSOB finds the Pareto optimal weights for OOB and UOB at each time step, to maximize minority class recall and majority class recall simultaneously. Experiments on five real world data applications show that MOSOB performs well in both static and dynamic data streams. Furthermore, we look into its performance on a group of highly imbalanced data streams. To respond to the minority class within 10000 time steps, the imbalance rate can be as low as 0.1% for easy data streams; at least 3% of imbalance rate is required to classify difficult data streams.
Genomic and archaeological evidence suggest a dual origin of domestic dogs
Venue: Science
Year: 2016

A dogged investigation of domestication The history of how wolves became our pampered pooches of today has remained controversial. Frantz et al. describe high coverage sequencing of the genome of an Irish dog from the Bronze Age as well as ancient dog mitochondrial DNA sequences. Comparing ancient dogs to a modern worldwide panel of dogs shows an old, deep split between East Asian and Western Eurasian dogs. Thus, dogs were domesticated from two separate wolf populations on either side of the Old World. Science, this issue p. 1228 Dogs may have been domesticated independently in Eastern and Western Eurasia from distinct wolf populations. The geographic and temporal origins of dogs remain controversial. We generated genetic sequences from 59 ancient dogs and a complete (28x) genome of a late Neolithic dog (dated to ~4800 calendar years before the present) from Ireland. Our analyses revealed a deep split separating modern East Asian and Western Eurasian dogs. Surprisingly, the date of this divergence ~14,000 to 6400 years ago) occurs commensurate with, or several millennia after, the first appearance of dogs in Europe and East Asia. Additional analyses of ancient and modern mitochondrial DNA revealed a sharp discontinuity in haplotype frequencies in Europe. Combined, these results suggest that dogs may have been domesticated independently in Eastern and Western Eurasia from distinct wolf populations. East Eurasian dogs were then possibly transported to Europe with people, where they partially replaced European Paleolithic dogs.
Investigation on Chalcogenide Glass Additive Manufacturing for Shaping Mid infrared Optical Components and Microstructured Optical Fibers
Year: 2021

In this work, an original way of shaping chalcogenide optical components has been investigated. Thorough evaluation of the properties of chalcogenide glasses before and after 3D printing has been carried out in order to determine the impact of the 3D additive manufacturing process on the material. In order to evaluate the potential of such additive glass manufacturing, several preliminary results obtained with various chalcogenide objects and components, such as cylinders, beads, drawing preforms and sensors, are described and discussed. This innovative 3D printing method opens the way for many applications involving chalcogenide fiber elaboration, but also many other chalcogenide glass optical devices.
Brainwriting and Idea Selection in a Crowd Supported Multi Touch Table and Tablet Environment
Year: 2019

We propose an adaption and extension of the 6 3 5 brainwriting method using tablet computers and a multi touch table. This table and tablet environment supports the group as well as the individual throughout the ideation process and is complemented by a website that incorporates crowd ratings into the process. Doing this, we improve the traditional 6 3 5 method, minimizing organizational efforts, adding anonymity and integrating external, crowd powered input into the process. We also advance the method to include a group tool for sorting and evaluating ideas, as well as providing valuable documentation of ideation processes. Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third party components of this work must be honored. For all other uses, contact the owner/author(s) CHI'19 Extended Abstracts, May 4 9, 2019, Glasgow, Scotland, UK.
Design of High Frequency Trading Algorithm Based on Machine Learning
Year: 2019

Based on iterative optimization and activation function in deep learning, we proposed a new analytical framework of high frequency trading information, that reduced structural loss in the assembly of Volume synchronized probability of Informed Trading $VPIN$ Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and Support Vector Machine (SVM) to make full use of the order book information. Amongst the return acquisition procedure in market making transactions, uncovering the relationship between discrete dimensional data from the projection of high dimensional time series would significantly improve the model effect. $VPIN$ would prejudge market liquidity, and this effectiveness backtested with CSI300 futures return.
The role of infrastructure in economic growth and income inequality in Indonesia
Venue: Economics Sociology
Year: 2020

The relationship between infrastructure development, economic growth, and income inequality has always been debated. Those debates raised the question of "is there a role for infrastructure development in economic growth which in turn encourages a decline in income inequality?" This study aims to analyze the direct effect of infrastructure development on economic growth and the indirect effect on income inequality. The present study used the Gini Ratio, Gross Regional Domestic Product (GRDP) data at constant market prices, investment, number of workforces, percentage of poor population, distribution of clean water, electricity distribution, and road length as of 2010 2016. The analytical methods applied here include descriptive method along with a twostep regression analysis method. The results reveal that infrastructure had a positive effect on economic growth, whereas direct economic growth harmed income inequality. These findings demonstrate that infrastructure indirectly reduces income inequality. Thus, infrastructure development, especially basic infrastructure and transportation, could reduce income inequality in Indonesia. Based on these findings, the government and related parties should encourage investment in basic infrastructure and transportation to improve economic performance sustainably.