nils-herrmann/clean_scirepeval huggingface dataset:
Identifying factors affecting the safety of mid block bicycle lanes considering mixed 2 wheeled traffic flow
Venue: Traffic injury prevention
Year: 2017

Electric bikes (e bikes) have been one of the fastest growing trip modes in Southeast Asia over the past 2 decades. The increasing popularity of e bikes raised some safety concerns regarding urban transport systems. The primary objective of this study was to identify whether and how the generalized linear regression model (GLM) could be used to relate cyclists' safety with various contributing factors when riding in a mid block bike lane. The types of 2 wheeled vehicles in the study included bicycle style electric bicycles (BSEBs) scooter style electric bicycles (SSEBs) and regular bicycles (RBs) Methods: Traffic conflict technology was applied as a surrogate measure to evaluate the safety of 2 wheeled vehicles. The safety performance model was developed by adopting a generalized linear regression model for relating the frequency of rear end conflicts between e bikes and regular bikes to the operating speeds of BSEBs, SSEBs, and RBs in mid block bike lanes.  The frequency of rear end conflicts between e bikes and bikes increased with an increase in the operating speeds of e bikes and the volume of e bikes and bikes and decreased with an increase in the width of bike lanes. The large speed difference between e bikes and bikes increased the frequency of rear end conflicts between e bikes and bikes in mid block bike lanes. A 1% increase in the average operating speed of e bikes would increase the expected number of rear end conflicts between e bikes and bikes by 1.48% A 1% increase in the speed difference between e bikes and bikes would increase the expected number of rear end conflicts between e bikes/bikes by 0.16%  The conflict frequency in mid block bike lanes can be modeled using generalized linear regression models. The factors that significantly affected the frequency of rear end conflicts included the operating speeds of e bikes, the speed difference between e bikes and regular bikes, the volume of e bikes, the volume of bikes, and the width of bike lanes. The safety performance model can help better understand the causes of crash occurrences in mid block bike lanes.
Digital Image Processing An Algorithmic Introduction using Java
Venue: Texts in Computer Science
Year: 2008

This revised and expanded new edition of an internationally successful classic presents an accessible introduction to the key methods in digital image processing for both practitioners and teachers. Emphasis is placed on practical application, presenting precise algorithmic descriptions in an unusually high level of detail, while highlighting direct connections between the mathematical foundations and concrete implementation. The text is supported by practical examples and carefully constructed chapter ending exercises drawn from the authors' years of teaching experience, including easily adaptable Java code and completely worked out examples. Source code, test images and additional instructor materials are also provided at an associated website. Digital Image Processingis the definitive textbook for students, researchers, and professionals in search of critical analysis and modern implementations of the most important algorithms in the field, and is also eminently suitable for self study.
Influence of Autonomous and Connected Vehicles on Stability of Traffic Flow
Year: 2015

The driving environment is expected to change by the introduction of connected and autonomous vehicles. These technologies are intended to improve safety and mobility in transportation systems. Connected Vehicle technology provides real time information about surrounding traffic condition as well as guidance from the traffic management center. This additional information is expected to improve drivers' efficiency, response, and comfort while improving safety and mobility. Connected Vehicle technology can also improve the efficiency and reliability of operating autonomous vehicles, even though the latter could operate with their on board sensors and without communication. Unfortunately, most modeling approaches in the literature do not distinguish between connectivity and automation. Therefore, there is a need for a comprehensive acceleration framework to distinguish between these two concepts while modeling this new driving environment. The authors previously presented a framework that utilizes different models with different assumptions to simulate several vehicle types with different communication capabilities (1) This paper presents a stability analysis of this framework for different market penetration rates of connected and autonomous vehicles. The stability analysis reveals that connected and autonomous vehicles can improve string stability. Moreover, automation can be expected to be more effective in preventing shockwave formation and propagation
Challenges of Debugging Software Errors for Automatic Program Repair
Year: 2018

This paper investigates the problem of software errors concentrating on the newborn research area of automatic program repair. It aims at providing a roadmap toward the research in automatic software debugging in general, and at laying bare the status quo of automatic program repair, in particular. The authors highlight the issues in software development lifecycle that give rise to introduce errors. Then, the authors elucidate which issues can be resolved and which ones not. The authors review the impediments that preclude resolving every error and releasing correct software. The authors give the strategies to eliminate software errors and the concomitant limitations to these strategies. Then, the authors shift the focus on the problem of automatic program repair which is an integral part for full automatic software debugging. Considering the status quo, the paper elaborates on some of the significant challenges afflicting this avenue of research.
Deep learning predicts tuberculosis drug resistance status from genome sequencing data
Year: 2018

The diagnosis of multidrug resistant and extensively drug resistant tuberculosis is a global health priority. Whole genome sequencing of clinical Mycobacterium tuberculosis isolates promises to circumvent the long wait times and limited scope of conventional phenotypic antimicrobial susceptibility, but gaps remain for predicting phenotype accurately from genotypic data.  Using targeted or whole genome sequencing and conventional drug resistance phenotyping data from 3,601 Mycobacterium tuberculosis strains, 1,228 of which were multidrug resistant, we investigated the use of machine learning to predict phenotypic drug resistance to 10 anti tuberculosis drugs. The final model, a multitask wide and deep neural network (MD WDNN) achieved improved high predictive performance: the average AUCs were 0.979 for first line drugs and 0.936 for second line drugs during repeated cross validation. On an independent validation set, the MD WDNN showed average AUCs, sensitivities, and specificities, respectively, of 0.937, 87.9% and 92.7% for first line drugs and 0.891, 82.0% and 90.1% for second line drugs. In addition to being able to learn from samples that have only been partially phenotyped, our proposed multidrug architecture shares information across different anti tuberculosis drugs and genes to provide a more accurate phenotypic prediction. We use t distributed Stochastic Neighbor Embedding (t SNE) visualization and feature importance analyses to examine inter drug similarities.  Machine learning is capable of accurately predicting resistant status using genomic information and holds promise in bringing sequencing technologies closer to the bedside.
Mountaineering or Ratcheting? Stone Age Hunting Weapons as Proxy for the Evolution of Human Technological, Behavioral and Cognitive Flexibility
Year: 2016

Cultural, behavioral and cognitive evolution is often seen as cumulative and sometimes referred to in terms of a ratchet or the ratchet effect. In this contribution, I assess the value of the ratchet analogy as blanket explanation for the above aspects of human evolution. I use Stone Age weapon technologies as proxy for the evolution of human technological, behavioral and cognitive flexibility, and by doing so show that the ratchet analogy falls short of explaining human variability and complexity as reflected in the Stone Age archaeological record. Considering human cultural, behavioral and cognitive evolution from a theoretically constructed rugged landscape point of view, I suggest that mountaineering may be a more suitable analogy for the accumulation of human culture. In this scenario, culture and technology anchor societies within their respective evolutionary trajectories and fitness landscapes, and it more accurately reflects humans as 'masters of flexibility'
Examining the Effects of Employee Empowerment, Teamwork, and Employee Training on Job Satisfaction
Year: 2016

Building job satisfaction is undoubtedly one of the objectives for any organization. This study is designed to test the effects of employee empowerment, teamwork, and employee training on job satisfaction in higher education sector. The data were collected from 242 employees of public universities in northern Malaysia using an online survey. The collected data were analysed using structural equation modelling on AMOS. The findings indicated that employee empowerment has a significant positive effect on job satisfaction. It was also found that teamwork has a significant positive effect on job satisfaction. Finally, the findings revealed that employee training has a significant positive effect on job satisfaction.
Proximity vital sign sensor using self oscillating mixer
Venue: 2016 URSI Asia Pacific Radio Science Conference (URSI AP RASC)
Year: 2016

This paper presents a design of proximity vital sign sensor using a self oscillating mixer (SOM) at 2.45 GHz. The proposed system consists of a planar resonator that determines the oscillation frequency and a transistor that functions not only as an oscillator but also as a mixer. Note that the supply voltage of the transistor is in pinch off region to maximize non linearity. The input impedance of the resonator is varied due to the distance variation between the human body and the resonator. This variation of input impedance changes the oscillation frequency of the proposed sensor. Then, this frequency deviation is self mixed in the transistor. It can be detected at the emitter in a form of DC voltage, because the proposed system is a kind of direct conversion receiver. So, if we track the emitter voltage, we can obtain the vital sign of the human subject. The measurement results confirm that the proposed sensor can detect the vital sign at 20 mm away from a subject.
Inverse GANs for accelerated MRI reconstruction
Venue: Optical Engineering Applications
Year: 2019

State of the art algorithms for accelerated magnetic resonance image (MRI) reconstruction are nowadays dominated by deep learning based techniques. However, the majority of these methods require the respective sampling patterns in training, which limits their application to a specific problem class. We propose an iterative reconstruction approach that incorporates the implicit prior provided by a generative adversarial network (GAN) which learns the probability distribution of uncorrupted MRI data in an off line step. Since the unsupervised training of the GAN is completely independent of the measurement process, our method is in principle able to address multiple sampling modalities using a single pre trained model. However, it turns out that the desired target images potentially lie outside the range space of the learned GAN, leading to reconstructions that resemble the target images only at a coarse level of detail. To overcome this issue, we propose a refinement scheme termed GAN prior adaption, that allows for additional adaption of the generating network with respect to the measured data. The proposed method is evaluated on multi coil knee MRI data for different acceleration factors and compared to a classical as well as a deep learning based approach showing promising quantitative and qualitative results.
Aegis International and Ballistic Missile Defense: A New Interoperability Network
Year: 2011

The scope of the ballistic missile threat facing the United States and our allies is constantly expanding. Rising powers such as China and India have invested heavily in conventional and strategic strike capability, while rogue nations including North Korea and Iran continue to press their missile programs forward. Non state actors have also joined in the game. According to the Department of Defense's 2010 Ballistic Missile Defense Review (BMDR) the ballistic missile threat will likely continue to increase over the next decade. Ballistic Missile Defense (BMD) has been called the greatest systems engineering challenge ever undertaken. Hitting a "bullet with a bullet" has challenged policy, military and technical professionals for decades. But today, a U.S. National Ballistic Missile Defense (National BMDS) capability has emerged and a new, Aegis international taxonomy has opened the door for an international ballistic missile defense umbrella. The push to enhance U.S. and international BMD capabilities is being supported by innovative, collaborative initiatives. We highlight several of these projects, and show how they are charting the course of future BMD capabilities by focusing on the interoperability of BMD missions. This interoperability will ultimately ensure the success of DoD's Phased Adaptive Approach to missile defense.
Orocutaneous Fistula or Traumatic Infectious Skin Lesion: A Diagnostic Dilemma
Venue: Case reports in dentistry
Year: 2015

Orocutaneous fistula (OCF) (of dental origin) is an uncommon but well described condition in the literature. These are often misdiagnosed by physicians and dentists. Careful selection of investigating modality is important in case of diagnostically challenging cases. A 19 year old female came with a complaint of a lesion on the chin reported with h/o trauma with the impact on chin presented as diagnostic dilemma because of unusual case history and clinical examination. Commonly used radiographic investigations like IOPA and orthopantomograph did not resolve the dilemma whereas advanced imaging modality like CT scan, 3D volume imaging, and contrast enhanced CT played an important role in the diagnosis of OCF and selecting the treatment plan.
Wafa Idris ou la mythification du terrorisme feminin (Jerusalem, 2002)
Year: 2019

Wafa Idris is the first Palestinian woman who committed a suicide attack in Palestine. On January 27th, 2002, she blew herself up in a downtown Jerusalem shopping mall, killing one Israeli man and wounding several others. As the first female martyr to die for the Palestinian cause, her death has reverberated both at the national and the international level. In the aftermath of her death, Arabic medias glorified Idris and hailed her as the new breed of Palestinian heroes. The progressive mythification of Wafa Idris will be shown in this article.
Relativistic fluid dynamics with spin
Year: 2018

Using the conservation laws for charge, energy, momentum, and angular momentum, we derive hydrodynamic equations for the charge density, local temperature, and fluid velocity, as well as for the spin tensor, starting from local equilibrium distribution functions for particles and antiparticles with spin 1/2. The resulting set of differential equations extend the standard picture of perfect fluid hydrodynamics with a conserved entropy current in a minimal way. This framework can be used in space time analyzes of the evolution of spin and polarization in various physical systems including high energy nuclear collisions. We demonstrate that a stationary vortex, which exhibits vorticity spin alignment, corresponds to a special solution of the spin hydrodynamical equations.
Three dimensional quantitative analysis of amyloid plaques in the whole brain with high voxel resolution
Year: 2019

Amyloidosis of the central nervous system is one of the main pathological hallmarks of Alzheimer's disease (AD) Changes in the number and the load of amyloid plaque (Ab) deposition are important indicators of the progression of AD. However, the quantitative analysis of amyloidosis at the subcellular level in the whole brain remains a substantial challenge. In this study, an automatic analysis method was established for assessing amyloid plaques in the whole brain, including convenient whole brain labeling, imaging with a fluorescence micro optical sectioning tomography system (fMOST) and an automated three dimensional (3D) analysis method. Validation using immunostaining showed that the detection rate of amyloid plaques larger than 10 um was 97.71% 0.18% We achieved a 3D dataset of amyloid plaques in the whole brain of a 5XFAD transgenic mouse at a resolution of 0.32 mmx0.32 mmx2 mm and quantitatively analyzed the 3D distribution of amyloid plaques in different brain regions and nuclei through automated registration, segmentation and counting. The results indicated that the combination of the fMOST system with a whole brain staining method enables the acquisition and quantitative analysis of a brain wide dataset of amyloid plaques, which may contribute to elucidating the underlying pathogenesis of AD and therapeutic drug discovery.
Managing Business Process Variability Through Process Mining and Semantic Reasoning: An Application in Healthcare
Venue: PRO VE
Year: 2017

Managing process variability enable the process model adaptability according changes in the application environment. In the healthcare area, flexibility is essential to provide a quality treatment because, even patients with the same diagnostic, may follow different paths and suffer different proceedings. Besides, there are many aspects to be considered for the selection of a path, as patient's symptoms, and clinical guidelines, among others. In this context, this research presents a framework for the management of the process variants through semantic reasoning. The enrichment of the business process with semantics enables the automation of the configuration, thus promoting more flexible and adaptive solutions. The proposed framework helps selecting the appropriate process variant according the patient's symptoms by reasoning on ontologies based known expertise. In our specific use case, we will use the expertise of the Brazilian guideline for acute ischemic stroke.
The Evolution of the South African System of Innovation Since 1916
Year: 2009

In this book, Mario Scerri provides the reader with a novel and sweeping rendition of South Africa's economic history from the early part of the twentieth century to the present. He applies a broad innovation systems approach to this history spanning the period from 1916, which saw the drafting of South Africa's first Science and Technology plan to the first fourteen years of the post apartheid period. The introduction of the book lays out the scope of the work and its focus on the identification of continuities and ruptures in the economic history of South Africa. The first part of the book deals with the theoretical foundations of the approach. The first chapter in this section looks at the emergence of evolutionary economics and innovation systems theory as the basis for the main countervailing argument against the neoclassical/neoliberal orthodoxy. In the course of this chapter the foundation is laid for the development of an alternative general theory of economics. The second chapter covers the main debates on the economic history of South Africa and looks at the several varieties of the liberal and Marxist renditions of this history. The theoretical section lays the foundation for the history that is covered in the four chapters which follow. These cover three broad periods since 1916. The first runs up to 1948 with the election victory of the National Party. The second covers the apartheid period and the last follows with an account of the post apartheid political economy. An endnote provides the basis for the analysis of what may possibly be the emergence of a fourth main period in the evolution of the South African system of innovation. The Evolution of the South African System of Innovation since 1916 opens up a novel engagement with the complex phenomenon of apartheid, its genealogy and its aftermath. It will appeal to economists and economic historians who are interested in the economy of South Africa. It will be of particular interest to evolutionary economists who use the systems of innovation approach as an alternative to mainstream neoclassical economics in the analysis of dynamic economic systems. For this particular audience, this book will provide a welcome addition to the growing body of literature in this area, especially given the novelty of its historical approach.
Balanced Word Clusters for Interpretable Document Representation
Venue: 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)
Year: 2019

We present Bag of Balanced Concepts (BOBC) a document representation method for fuzzy and interpretable similarity estimation based on word clusters. For this purpose, a k medoid variant is proposed, which iteratively resamples small clusters to introduce a tendency towards balanced cluster sizes. The necessary inter word similarities for clustering are computed using GloVe or word2vec word embeddings. In this way, words that often share contexts tend to appear in the same clusters. Those clusters are used to represent documents as normalized probability distributions. Various distance measures acting as document dissimilarity estimators have been evaluated on five datasets. The impact of clustering parameters, input word vectors, and inverse document frequency weighting has been examined in our experiments. Furthermore, a comparison with document similarity estimation baselines has been performed. We demonstrate that, on average, our approach outperforms cosine similarity of both weighted Bag of Words vectors (TF IDF and BM25) and word embedding centroids (Word Centroid Distance)
Carvacrol and thymol: strong antimicrobial agents against resistant isolates
Year: 2017

Antibiotics have been effective in treatment of infectious diseases, but resistance to these drugs has led to the severe consequences. In recent years, medicinal herbs have been used for the prevention and protection against infectious diseases. Thymol and carvacrol are active ingredients of family Lamiaceae; these components have antibacterial and antifungal effects. In this review, we survey antimicrobial properties of carvacrol and thymol. Available data from different studies (microbiological, retrieve from PubMed, and Scopus databases) about antimicrobial affects carvacrol and thymol was evaluated. Carvacrol and thymol can inhibit the growth of both gram positive and gram negative bacteria. These compounds have antifungal and antibiofilm effects. Thymol and carvacrol can also be applied as an alternative antimicrobial agent against antibiotic resistant pathogenic bacteria. Thus, thymol and carvacrol are recommended for potential medical use; however, more research is required on toxicity and side effects of the compounds.