nils-herrmann/clean_scirepeval huggingface dataset:
Image splicing detection based on general perspective constraints
Venue: 2015 IEEE International Workshop on Information Forensics and Security (WIFS)
Year: 2015

Image Forensics offers numerous solutions for authenticating the contents of digital images. Unfortunately most of these technologies are ready to work only in controlled environments and their performance heavily drop when applied in real world scenario (e.g, social network, low resolution images, where the images have gone through a chain of unknown processes. In this paper we present a method for forgery detection based on perspective constraints; similar techniques have been proposed in the past but they are effective only when the image is captured with no tilt and no roll thus been unusable in most natural scenes. Here, this solution is extended to include these cases, and we show its applicability even when the image is exchanged through a social network (specifically Facebook and Twitter) where the image is subjected to heavy compression and resizing.
A Cyber Physical Systems architecture for Industry 4.0 based manufacturing systems
Year: 2015

Recent advances in manufacturing industry has paved way for a systematical deployment of Cyber Physical Systems (CPS) within which information from all related perspectives is closely monitored and synchronized between the physical factory floor and the cyber computational space. Moreover, by utilizing advanced information analytics, networked machines will be able to perform more efficiently, collaboratively and resiliently. Such trend is transforming manufacturing industry to the next generation, namely Industry 4.0. At this early development phase, there is an urgent need for a clear definition of CPS. In this paper, a unified 5 level architecture is proposed as a guideline for implementation of CPS.
Cross Domain Few Shot Learning with Meta Fine Tuning
Venue: ArXiv
Year: 2020

In this paper, we tackle the new Cross Domain Few Shot Learning benchmark proposed by the CVPR 2020 Challenge. To this end, we build upon state of the art methods in domain adaptation and few shot learning to create a system that can be trained to perform both tasks. Inspired by the need to create models designed to be fine tuned, we explore the integration of transfer learning (fine tuning) with meta learning algorithms, to train a network that has specific layers that are designed to be adapted at a later fine tuning stage. To do so, we modify the episodic training process to include a first order MAML based meta learning algorithm, and use a Graph Neural Network model as the subsequent meta learning module. We find that our proposed method helps to boost accuracy significantly, especially when combined with data augmentation. In our final results, we combine the novel method with the baseline method in a simple ensemble, and achieve an average accuracy of 73.78% on the benchmark. This is a 6.51% improvement over existing benchmarks that were trained solely on miniImagenet.
A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings
Venue: Measurement
Year: 2019

Fault diagnosis is an important technology in the development of modern industrial safety. Vibration information is commonly used to determine the state of bearings. Driven by big data, deep learning brings new opportunities to fault diagnosis. As an unsupervised deep learning algorithm, a stacked autoencoder (SAE) can relieve the pressure of labelling data. Due to the diversity and variability of the actual fault diagnosis distribution, an optimized transfer learning (TL) algorithm is proposed to solve the domain adaptation. By directly inheriting features obtained from the pre training process in the source domain and changing only the fine tuning process, the complexity of the algorithm is reduced. Considering the data reconstruction ability and robustness, a sparse stacked denoising autoencoder (SSDAE) is proposed for feature extraction, which can indirectly improve the diagnostic accuracy in the target domain. The results for data from the Case Western Reserve University Bearing Data Center show that the proposed SSDAE TL algorithm is feasible and easy to implement for the fault diagnosis of bearings.
ICT for socio economic development: A citizens' perspective
Venue: Inf. Manag.
Year: 2018

There is a widespread belief that information and communication technologies (ICTs) can play a significant role in the socio economic development of a developing country. ICT has the potential to affect many aspects of economic and societal activities such as GDP growth, employment, productivity, poverty alleviation, quality of life, education, and healthcare. While the literature provides a myriad of definitions and elements of socio economic development, the focus tends to be on theoretical conceptualizations from various disciplines and impacts from isolated individual projects. In particular, the impact of ICT on socio economic development has not been carefully examined from the viewpoint of the ultimate stakeholder, the citizens of a country, who are the final consumers of the technology. This study fills this gap by focusing on the citizens' view in describing ICT driven socio economic development in a developing country. A theoretical framework influenced by the "capabilities approach" was developed to guide this research, and the interpretive stance was used to conduct the study. More specifically, the narrative research method which is seldom used in IS research but is appropriate for this study, was used. Narratives allow deeper and profound insights into social representations and participants' beliefs about the role of ICT in socio economic development. Using this methodology, a model of the impact dimensions of socio economic development is presented.
Learning Stable Nonparametric Dynamical Systems with Gaussian Process Regression
Venue: ArXiv
Year: 2020

Modelling real world systems involving humans such as biological processes for disease treatment or human behavior for robotic rehabilitation is a challenging problem because labeled training data is sparse and expensive, while high prediction accuracy is required from models of these dynamical systems. Due to the high nonlinearity of problems in this area, data driven approaches gain increasing attention for identifying nonparametric models. In order to increase the prediction performance of these models, abstract prior knowledge such as stability should be included in the learning approach. One of the key challenges is to ensure sufficient flexibility of the models, which is typically limited by the usage of parametric Lyapunov functions to guarantee stability. Therefore, we derive an approach to learn a nonparametric Lyapunov function based on Gaussian process regression from data. Furthermore, we learn a nonparametric Gaussian process state space model from the data and show that it is capable of reproducing observed data exactly. We prove that stabilization of the nominal model based on the nonparametric control Lyapunov function does not modify the behavior of the nominal model at training samples. The flexibility and efficiency of our approach is demonstrated on the benchmark problem of learning handwriting motions from a real world dataset, where our approach achieves almost exact reproduction of the training data.
A Study of Feature Selection and Extraction Algorithms for Cancer Subtype Prediction
Venue: ArXiv
Year: 2021

In this work, we study and analyze different feature selection algorithms that can be used to classify cancer subtypes in case of highly varying high dimensional data. We apply three different feature selection methods on five different types of cancers having two separate omics each. We show that the existing feature selection methods are computationally expensive when applied individually. Instead, we apply these algorithms sequentially which helps in lowering the computational cost and improving the predictive performance. We further show that reducing the number of features using some dimension reduction techniques can improve the performance of machine learning models in some cases. We support our findings through comprehensive data analysis and visualization.
Ending Pandemics: US Foreign Policy to Mitigate Today's Major Killers, Tomorrow's Outbreaks, and the Health Impacts of Climate Change.
Year: 2019

Every U.S. President in recent decades has had to respond to at least one pandemic disease. Political leadership has proven decisive. In the coming years, U.S. foreign policy will face at least three inter related issues: today's major pandemics of AIDS, TB, and Malaria; future outbreaks with the potential to become pandemics; and rising risk from infectious diseases associated with climate change. A review of epidemiologic data shows global progress on each issue is threatened. A coordinated U.S. effort, across agencies and engaged with national and multilateral partners, could save lives and address significant foreign policy interests. Such an effort could boost economic prosperity by reducing disease related lost productivity, which we estimate at $1.7 trillion, with returns to investment in pandemic related global health efforts averaging 17 20 to 1. Foreign policy focus on pandemics could also address economic and social inequalities and support climate adaptation and mitigation. Pandemicrelated global health spending is 0.19% of the U.S. budget a figure that has been flat in recent years even with growing needs and significant potential gains from investment. Each U.S. President since Jimmy Carter has had to respond to an international outbreak of infectious disease often multiple  While pandemics are biological phenomena, they are highly amenable to political action through attention, policymaking, and resource allocation. President Reagan's inaction contributed to the growth of the global AIDS pandemic, while President George W. Bush's engagement helped stem its upward trajectory.1 President Obama's intervention on Ebola, albeit belated, helped galvanize coordinated international action to bring the epidemic under control.2 Today, from AIDS to Ebola to mosquito borne diseases, political action is insufficient. The struggle to respond to the current Ebola outbreak in the Democratic Republic of Congo, the second most lethal in history, illustrates the
Combination Therapy Using Radiofrequency Microneedling and Corticosteroids for Hypertrophic Scars: A Case Report.
Venue: The Journal of clinical and aesthetic dermatology
Year: 2020

Dysregulated scar formation can cause hypertrophic scarring, a difficult entity to treat. Although fractionated lasers are a popular therapy for hypertrophic scars, laser induced epidermal damage might cause pigmentary changes in individuals with darker skin tones. Radiofrequency microneedling delivers ablative energy directly to the dermis and is safe for use in such patients. We present a case of hypertrophic scarring in a patient with Fitzpatrick Skin Type IV treated with a combination of radiofrequency microneedling, topical and intralesional corticosteroids, and intralesional onabotulinumtoxinA. Three weeks posttreatment, scars were significantly improved in both color and texture. The patient did not report any side effects. Based on these results, this novel combination therapy appears to be a promising treatment modality for hypertrophic scars in patients with Fitzpatrick Skin Types IV to VI. However, further large scale studies will need to be completed to determine long term efficacy and possible adverse events.
Effectiveness of a pediatric palliative home care team as experienced by parents and health care professionals.
Venue: Journal of palliative medicine
Year: 2012

Little data are available on palliative home care for children. The objective of this study was to evaluate the effectiveness of a specialized pediatric palliative home care team (PPHCT) as experienced by parents and health care professionals (HCPs) METHODS Parents and HCPs involved in the care of terminally ill children who died and whom the PPHCT was in charge of were surveyed with questionnaires focusing on satisfaction with the PPHCT, satisfaction with the course of the dying phase, and the development of anxiety, depression, and prolonged grief disorder.  Forty three parent dyads participated (return rate, 88% Satisfaction with the PPHCT scored a median of 10 (numeric rating scale, 0 10) The child's death was predominantly experienced as very peaceful (median, 9) 71% died at home. According to parents, involvement of the PPHCT led to highly significant (p<0.001) improvements in the children's symptoms and quality of life, as well as in aspects of communication and administrative barrier reduction. Anxiety was detected in 25% of parents, depression in 19% and prolonged grief disorder in 13% HCPs (return rate, 83% evaluated all investigated care domains (particularly cooperation/communication/family support) as being significantly improved (p<0.001) Thirty five percent of HCPs felt uncertain concerning pediatric palliative care; 79% would welcome specific training opportunities.  Involvement of a PPHCT is experienced as a substantial improvement of care by parents and HCPs. Coordination of palliative care during the last phase of life appears to be an important quality factor for the home care of dying children and their families.
Spin orbit effect into isomerization barrier of small gold Clusters. Oh D2h Fluxionality of the Au62+ cluster Investigated by relativistic methods
Year: 2017

The O h [Au 6 2+ cluster exhibits an open shell 1 s 2 1 p 2 which trend to a more stable D 2 h isomer in 31.5 kcal/mol, as observed in the experimental [Au 6 {P(C 6 H 4 Me o)pH 2 6 cluster. By taking into account the spin orbit coupling (SOC) in O h [Au 6 2+ a resulting 1 s 1/2 2 1 p 1/2 2 closed shell superatomic configuration is obtained stabilizing such structure by about 14.7 kcal/mol, decreasing the isomerization barrier. Thus, the spin orbit term favors the O h D 2 h conformation rearrangement depicting a decrease in the calculated energy difference between both conformations, an interesting consequence which is not obtained in the hypothetical lighter counterparts.
Towards the development of a minimal anthropomorphic robot hand
Venue: 2014 IEEE RAS International Conference on Humanoid Robots
Year: 2014

Many robot hands in the literature try to achieve full kinematic anthropomorphism, and as such are often very complex and difficult/expensive to produce. This paper follows recent work that predicts that high dexterity can also be achieved through a minimal (reduced) anthropomorphic design. New experimental and simulation results that optimize grasping performance for a minimal hand are presented. A first prototype of the hand, incorporating the optimized kinematics as well as innovative endoskeletal mechanical and actuation architectures, has been designed. The robot hand prototype has been fabricated using fused deposition modelling technology, and is evaluated with respect to its grasping performance.
D3R grand challenge 4: blind prediction of protein ligand poses, affinity rankings, and relative binding free energies
Venue: Journal of Computer Aided Molecular Design
Year: 2020

The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4) GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 co crystal structures were released, and Stage 2 tested affinity rankings with co crystal structures. We provide an analysis of the results and discuss insights into determined best practice methods.
Shallow Water Bathymetry Mapping from UAV Imagery based on Machine Learning
Venue: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Year: 2019

The determination of accurate bathymetric information is a key element for near offshore activities, hydrological studies such as coastal engineering applications, sedimentary processes, hydrographic surveying as well as archaeological mapping and biological research. UAV imagery processed with Structure from Motion (SfM) and Multi View Stereo (MVS) techniques can provide a low cost alternative to established shallow seabed mapping techniques offering as well the important visual information. Nevertheless, water refraction poses significant challenges on depth determination. Till now, this problem has been addressed through customized image based refraction correction algorithms or by modifying the collinearity equation. In this paper, in order to overcome the water refraction errors, we employ machine learning tools that are able to learn the systematic underestimation of the estimated depths. In the proposed approach, based on known depth observations from bathymetric LiDAR surveys, an SVR model was developed able to estimate more accurately the real depths of point clouds derived from SfM MVS procedures. Experimental results over two test sites along with the performed quantitative validation indicated the high potential of the developed approach.
Mining approximate frequency itemsets over data streams based on hash and interesting degree
Year: 2012

Frequent itemsets mining,which is the basic in the field of data stream mining,has been paid more and more attention by researchers.Due to the uncertainties,continuities and large amount of data streams,many mining algorithms are difficult to deal with these dynamic data streams.In this paper, hashed table and the interesting degree of association rules are introduced,where the former is used to represent the synoptic data structure and the latter is applied to incorporate attention of customers.After that,a new frequent itemsets mining algorithm named MIFS HT(mining interesting frequent itemsets with hash table) is proposed.Comparing with lossy counting and a similar algorithm called mining frequent item sets over data streams by matrix(MISM for short),the result shows that MIFS HT is more effective both in time and space efficiency.
TAPER: Time Aware Patient EHR Representation
Venue: IEEE Journal of Biomedical and Health Informatics
Year: 2020

Effective representation learning of electronic health records is a challenging task and is becoming more important as the availability of such data is becoming pervasive. The data contained in these records are irregular and contain multiple modalities such as notes, and medical codes. They are preempted by medical conditions the patient may have, and are typically recorded by medical staff. Accompanying codes are notes containing valuable information about patients beyond the structured information contained in electronic health records. We use transformer networks and the recently proposed BERT language model to embed these data streams into a unified vector representation. The presented approach effectively encodes a patient's visit data into a single a distributed representation, which can be used for downstream tasks. Our model demonstrates superior performance and generalization on mortality, readmission and length of stay tasks using the publicly available MIMIC III ICU dataset.
Trade Unions and the Minimum Wage
Year: 2018

Why do labor unions and left/labor parties support the statutory minimum wage in some countries, such as the United States, but oppose it in others, such as Denmark or Sweden? This paper presents a formal model of trade union preferences for the statutory minimum wage. On the one hand, the minimum wage can raise above minimum negotiated wages by increasing the union's fallback position and increasing the labor costs of nonunion competitor firms. On the other hand, the minimum wage presents certain dangers: it may (1) set wages higher or lower than unions prefer, (2) reduce the incentives of workers to join unions in the short term, and (3) undermine the social custom that sustains union membership in the long term. For these reasons, we predict that unions and left/labor parties will support a statutory minimum wage only when unions are too weak when unions bargain with a smaller share of firms, when they are legally restricted from engaging in certain strike actions, and wage bargaining is less coordinated to sustain high wages on their own. Empirically, we document a robust, cross national correlation between several of the key variables in our model and the type of minimum wage setting institution and illustrate the model's mechanisms using case studies of union preferences in the US, UK, Germany, and Sweden and party preferences in the UK and Germany.
Impact of Personality Traits and Entrepreneurship Education on Entrepreneurial Intentions of Business and Engineering Students
Venue: Sustainability
Year: 2019

Entrepreneurs bring an enormous contribution to a country's economic growth. Developing individual's interest into new venture creation represents an important asset, especially for less developed countries where entrepreneurial activities are fundamental in enhancing economic growth. The recent economic crisis turned the attention of European policymakers towards entrepreneurship as a driving force for the creation of new job opportunities, regional/national competitiveness and growth. The aim of this article is to verify what relations are between locus of control, need for achievement and entrepreneurial intention of youth, following a cognitive approach and how much entrepreneurial education consolidates entrepreneurial skills and informs the young that entrepreneurship is a career option. To analyse entrepreneurial intentions, locus of control, need for achievement and entrepreneurial education in the context of Romania, this research used a quantitative design based on the answers to the questionnaire conducted during March 2017 to October 2017 on a sample of 270 students from two important Romanian universities. From the methodological point of view, several hypotheses have been developed and tested using multivariate logistic regression estimates, frequency analysis, internal consistency reliability of the constructs and moderating effects. The results illustrate that locus of control, need for achievement and entrepreneurial education proved to be important determinants for venture creation among young students, both independently and under the action of control variables. Also, respondents' gender had a significant influence on one's intention of opening a business in the future, with males being more inclined to become entrepreneurs than females.
Reinforcing Cyber World Security with Deep Learning Approaches
Venue: 2020 International Conference on Communication and Signal Processing (ICCSP)
Year: 2020

In the past decade, the Machine Learning (ML) and Deep learning (DL) has produced much research interest in the society and attracted them. Now a days, the Internet and social life make a lead in most of their life but it has serious social threats. It is a challenging thing to protect the sensitive information, data network and the computers which are in unauthorized cyber attacks. For protecting the data's we need the cyber security. For these problems, the recent technologies of Deep learning and Machine Learning are integrated with the cyber attacks to provide the solution for the problems. This paper gives a synopsis of utilizing deep learning to enhance the security of cyber world and various challenges in integrating deep learning into cyber security are analyzed.