nils-herrmann/clean_scirepeval huggingface dataset:
Neighborhood co offending networks, structural embeddedness, and violent crime in Chicago
Venue: Soc. Networks
Year: 2017

Neighborhood disparities in crime are a persistent feature of U.S. cities. Scholars have documented that both local structural conditions and characteristics of spatially proximate communities influence neighborhood crime rates. Previous studies on neighborhood inequality in crime, however, are limited by their focus on identifying average spillover effects between pairs of spatially contiguous neighborhoods, and have neglected to consider how the broader social organization of the city influences local outcomes. This study examines the role of neighborhood level criminal networks in shaping the distribution of crime throughout cities. Employing arrest records and survey data from the Project on Human Development in Chicago Neighborhoods, we construct a neighborhood level co offending network for Chicago for 2001. We use this network to investigate how a focal neighborhood's homicide rate is influenced by its structural embeddedness within the larger inter neighborhood co offending network. Results indicate that a neighborhood's embeddedness increases the local homicide rate, even after controlling for the neighborhood's internal propensity toward crime and accounting for unobserved spatial processes.
Small regulatory bacterial RNAs regulating the envelope stress response
Venue: Biochemical Society transactions
Year: 2017

Most bacteria encode a large repertoire of RNA based regulatory mechanisms. Recent discoveries have revealed that the expression of many genes is controlled by a plethora of base pairing noncoding small regulatory RNAs (sRNAs) regulatory RNA binding proteins and RNA degrading enzymes. Some of these RNA based regulated processes respond to stress conditions and are involved in the maintenance of cellular homeostasis. They achieve it by either direct posttranscriptional repression of several mRNAs, including blocking access to ribosome and/or directing them to RNA degradation when the synthesis of their cognate proteins is unwanted, or by enhanced translation of some key stress regulated transcriptional factors. Noncoding RNAs that regulate the gene expression by binding to regulatory proteins/transcriptional factors often act negatively by sequestration, preventing target recognition. Expression of many sRNAs is positively regulated by stress responsive sigma factors like RpoE and RpoS, and two component systems like PhoP/Q, Cpx and Rcs. Some of these regulatory RNAs act via a feedback mechanism on their own regulators, which is best reflected by recent discoveries, concerning the regulation of cell membrane composition by sRNAs in Escherichia coli and Salmonella, which are highlighted here.
Learning RGB D Salient Object Detection Using Background Enclosure, Depth Contrast, and Top Down Features
Venue: 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
Year: 2017

In human visual saliency, top down and bottom up information are combined as a basis of visual attention. Recently, deep Convolutional Neural Networks (CNN) have demonstrated strong performance on RGB salient object detection, providing an effective mechanism for combining top down semantic information with low level features. Although depth information has been shown to be important for human perception of salient objects, the use of top down information and the exploration of CNNs for RGB D salient object detection remains limited. Here we propose a novel deep CNN architecture for RGB D salient object detection that utilizes both top down and bottom up cues. In order to produce such an architecture, we present novel depth features that capture the ideas of background enclosure, depth contrast and histogram distance in a manner that is suitable for a learned approach. We show improved results compared to state of the art RGB D salient object detection methods. We also show that the low level and mid level depth features both contribute to improvements in results. In particular, the F Score of our method is 0.848 on RGBD1000, which is 10.7% better than the current best.
Reported orofacial adverse effects of COVID 19 vaccines: The knowns and the unknowns
Venue: Journal of oral pathology medicine official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology
Year: 2021

Adverse events associated with vaccine administration can manifest in the oral cavity and orofacial region. Hence, the aim of this study was to compare the orofacial adverse effects of two recently authorised COVID 19 vaccines, namely BNT162b2 and mRNA 1273.  Publicly available data on BNT162b2 and mRNA 1273 vaccines were accessed from the relevant regulatory authorities in the United States, Canada, European Union and United Kingdom. Both patient/recipient information and healthcare professional fact sheets for each of these drugs were manually searched to find their orofacial adverse effects.  Adverse events affecting the orofacial region were reported for both vaccines. These were rare and included acute peripheral facial paralysis (Bell's palsy) facial swelling, and swelling of the lips, face or tongue associated with anaphylaxis. There was heterogeneity in the acknowledgement of vaccine related adverse events in North America compared with Europe.  Globally, there are inconsistencies in the description of adverse effects presenting in the orofacial region of the COVID 19 vaccines BNT162b2 and mRNA 1273. We believe that awareness of these orofacial manifestations will improve recognition, management and reporting of vaccine related adverse effects.
MicroPython or Arduino C for ESP32 Efficiency for Neural Network Edge Devices
Year: 2020

In the last few years, microcontrollers used for IoT devices became more and more powerful, and many authors have started to use them in machine learning systems. Most of the authors used them just for data collecting for ML algorithms in the clouds, but some of them implemented ML algorithms on the microcontrollers. The goal of this paper is to analyses the neural networks data propagation speed of one popular SoC (Espressif System company ESP32) with simple neural networks implementation with two different development environment, Arduino IDE and MycroPython. Neural networks with one hidden layer are used with a different number of neurons. This SoC is analysed because some companies started to produce them with UXGA (Ultra Extended Graphics Array) camera implemented and it can be used to distribute computing load from central ML servers.
Pharmaceutical Particle Engineering via Spray Drying
Venue: Pharmaceutical Research
Year: 2007

This review covers recent developments in the area of particle engineering via spray drying. The last decade has seen a shift from empirical formulation efforts to an engineering approach based on a better understanding of particle formation in the spray drying process. Microparticles with nanoscale substructures can now be designed and their functionality has contributed significantly to stability and efficacy of the particulate dosage form. The review provides concepts and a theoretical framework for particle design calculations. It reviews experimental research into parameters that influence particle formation. A classification based on dimensionless numbers is presented that can be used to estimate how excipient properties in combination with process parameters influence the morphology of the engineered particles. A wide range of pharmaceutical application examples low density particles, composite particles, microencapsulation, and glass stabilization is discussed, with specific emphasis on the underlying particle formation mechanisms and design concepts.
A Digital Twins Approach to Smart Grid Security Testing and Standardization
Venue: 2020 IEEE International Workshop on Metrology for Industry 4.0 IoT
Year: 2020

The exponential growth of the Internet of Things in recent years has created an ever larger cyber attack surface, introducing new security vulnerabilities for all computerized systems. Among the most significant of those systems are industrial control systems (ICS) consisting of many cyber physical components, and smart grids are a prominent example of ICS, whose failures have potential to cause major disruptions in all aspects of our daily lives. In this paper, we provide an overview of smart grid cybersecurity standards, and review major threats to smart grid environments at the physical, network and application layers. In order to overcome the current lack of standards for security evaluation of smart grids, we propose a digital twins based approach for the complete life cycle of a smart grid, which accurately models the functioning of the physical grid and avoids service disruptions caused by running security tests on the actual grid. A digital twins based approach is promising to provide a common ground for the development of standardized models for continuous and comprehensive penetration testing of smart grids.
Learning Deep Representations of Fine Grained Visual Descriptions
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2016

State of the art methods for zero shot visual recognition formulate learning as a joint embedding problem of images and side information. In these formulations the current best complement to visual features are attributes: manually encoded vectors describing shared characteristics among categories. Despite good performance, attributes have limitations: (1) finer grained recognition requires commensurately more attributes, and (2) attributes do not provide a natural language interface. We propose to overcome these limitations by training neural language models from scratch, i.e. without pre training and only consuming words and characters. Our proposed models train end to end to align with the fine grained and category specific content of images. Natural language provides a flexible and compact way of encoding only the salient visual aspects for distinguishing categories. By training on raw text, our model can do inference on raw text as well, providing humans a familiar mode both for annotation and retrieval. Our model achieves strong performance on zero shot text based image retrieval and significantly outperforms the attribute based state of the art for zero shot classification on the Caltech UCSD Birds 200 2011 dataset.
High Step Down Dual Output Light Emitting Diode Driver
Year: 2017

A conventional power factor correction (PFC) based light emitting diode (LED) drivers composed of two stage DC DC conversions has several drawbacks such as; increased system size due to more component count, less efficiency and complex control etc. The grid powered LED lighting demand for high step down conversion because the required voltage level of LED light is very less. Thus, this work proposes a one switch dual output (OSDO) coupled inductor buck (CIB) LED driver with current auto balancing. OSDO CIB converter can eliminate most of the drawbacks of conventional counterparts. The proposed OSDO CIB converter can provide various benefits such as; compact size, high efficiency, less total harmonic distortion (THD) simple control, and significant reduction in device voltage/current rating due to coupled inductors. The converter is designed with discontinuous conduction mode (DCM) of operation in order to achieve in phase current and voltage, high power factor (PF) and a low THD. This paper mainly emphasized on detailed operating modes and steady state analysis of proposed converter. Further, proto type of the converter is built and experimental validations are presented.
Bio hydrogen production from waste materials
Year: 2006

Hydrogen is a valuable gas as a clean energy source and as feedstock for some industries. Therefore, demand on hydrogen production has increased considerably in recent years. Electrolysis of water, steam reforming of hydrocarbons and auto thermal processes are well known methods for hydrogen gas production, but not cost effective due to high energy requirements. Biological production of hydrogen gas has significant advantages over chemical methods. The major biological processes utilized for hydrogen gas production are bio photolysis of water by algae, dark and photo fermentation of organic materials, usually carbohydrates by bacteria. Sequential dark and photo fermentation process is a rather new approach for bio hydrogen production. One of the major problems in dark and photo fermentative hydrogen production is the raw material cost. Carbohydrate rich, nitrogen deficient solid wastes such as cellulose and starch containing agricultural and food industry wastes and some food industry wastewaters such as cheese whey, olive mill and bakers yeast industry wastewaters can be used for hydrogen production by using suitable bio process technologies. Utilization of aforementioned wastes for hydrogen production provides inexpensive energy generation with simultaneous waste treatment. This review article summarizes bio hydrogen production from some waste materials. Types of potential waste materials, bio processing strategies, microbial cultures to be used, bio processing conditions and the recent developments are discussed with their relative advantages.
Representation of honour killings: critical discourse analysis of Pakistani English language newspapers.
Year: 2015

This study analysed newspaper coverage related to 'honour killings' in Pakistan using a critical discourse analysis approach. The objective was to elucidate the social effects of honour based murders from this discourse including the relationships of the incidents and the people represented. The study examined the reports of three Pakistani English language dailies that reiterate transecting norms relating to ethnicity, culture, class and gender. Such transections illustrate central values of Pakistani society in context of honour based murders as 'tribal, feudal and patriarchal. The study found that the overuse of the terms 'Honour Killing' and 'Karo Kari' are cliched in the newspapers portraying such murders; the overuse of which has formed legitimacy around the act that is not deserved. The newspaper production and depiction of honour based murder incidents as cultural, with its basis in the remnants of feudal, tribal and patriarchal only, is misleading and such interpretation conceals the possible connections with other social, economic, and political factors. The print media discourse presenting honour based murders, as incidences outside the framework of law and public policy, evades the problematisation of law and policy domains. The reporting and illustration of honour based murders were restricted to a victim perpetrator sphere that camouflages the involvement of other actors such as family, community and clan. The newspapers represent the discourse of honour based murder as a family/private matter, which provides a pretext to the state institutions including police, judiciary, and district administration to circumvent intervention. The analysis revealed that the newspapers have constructed and embodied the discourse related to the practice of honour based violence as a part of a cultural value system, in which 'family honour' is valued more than anything. This culturalised explanation makes it hard to challenge such argument, and the discourse limits the understanding of factors, mechanisms and actors that maintain and encourage such practices.
Regresi Logistik Pada Model Problem Based Learning Berbantu Software Cabri 3D
Year: 2020

Penelitian ini bertujuan untuk mengetahui seberapa besar pengaruh model Problem Based Learning berbantu software Cabri 3D dengan menggunakan regresi logistik biner terhadap kemampuan pemahaman konsep matematis siswa. Metode penelitian yang digunakan adalah Quasi Experimental Design dengan Posttest Only Control Design. Populasi penelitian ini mencakup seluruh siswa kelas VIII SMP Negeri 21 Kota Tangerang Selatan tahun pelajaran 2018/2019. Sampel penelitian sebanyak 67 siswa yang terbagi dalam dua kelas yang diambil dengan teknik cluster random sampling. Kedua kelas diberikan perlakuan yang berbeda. Kelompok eksperimen diberikan pembelajaran menggunakan model Problem Based Learning berbantu software Cabri 3D dan kelompok kontrol tidak diberikan pembelajaran menggunakan model Problem Based Learning berbantu software Cabri 3D. Hasil dari penelitian yang didapat menggunakan analisis regresi logistik, diperoleh model regresi logistik Uji signifikansi parameter yang digunakan, yaitu uji Rasio Likelihood dan uji Wald. Berdasarkan hasil uji serentak maupun uji parsial diperoleh hasil H0 ditolak artinya model Problem Based Learning berbantu software Cabri 3D berpengaruh signifikan terhadap kemampuan pemahaman konsep matematis siswa. Kemudian nilai odds ratio sebesar 2,880 menunjukan bahwa siswa yang menggunakan model Problem Based Learning berbantu software Cabri 3D memiliki peluang lulus kemampuan pemahaman konsep matematis sebesar kali dibandingkan siswa yang tidak menggunakan model problem based learning berbantu software Cabri 3D.
Knowledge Extraction from Natural Language Requirements into a Semantic Relation Graph
Venue: ICSE 2020
Year: 2020

A non exclusive, non transferable and limited right to use is granted. This document is intended solely for personal, non commercial use. Terms of Use (c) owner/authors 2020, publication rights licensed to ACM. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive version was published in IEEE/ ACM 42nd International Conference on Software Engineering Workshops (ICSEW'20) ACM ISBN 978 1 4503 7963 2,
Students' Critical Thinking Skills in Chemistry Learning Using Local Culture Based 7E Learning Cycle Model
Year: 2018

This research aimed at describing the effectiveness of the local culture based 7E learning cycle model in improving students' critical thinking skills in chemistry learning. It was an experimental research with post test only control group design. The population was the eleventh grade students of senior high schools in Singaraja, Indonesia. The sample included 111 students; 57 students as the experimental group and 54 students as the control group. The students in the experimental group learned through the local culture based 7E learning cycle model, whereas the ones in the control group learned through the discovery learning model. The data on the students' critical thinking skills were collected through the critical thinking skills test. The data were analyzed through descriptive and inferential analysis, namely: Independent Sample T Test for normal and homogeneous data, but Mann Whitney U Test for not normal and/or not homogeneous data. The results showed that the average score of students' critical thinking skills on the experimental and control groups were 64,5 and 55,3 respectively. The average score of the students' critical thinking skills on the experimental and control groups in the high level school were 70.0 and 58.4, respectively, but in low level school, they were 58.7 and 50.8 respectively.
Prevalence of multidrug resistance genes in Escherichia coli isolates from patients attending four hospitals in Minna, Nigeria
Year: 2020

This study determined the prevalence, antibiotic susceptibility and resistance genes among multidrug resistant Escherichia coli isolates from diarrheic patients in four hospitals within Minna, Nigeria.  Stool samples from one hundred and seven (107) diarrheic patients were collected and analysed to check for E. coli using spread plate techniques. The resistance pattern of the E. coli isolates to ten (10) antibiotics using disc diffusion techniques was determined. Five isolates with multidrug resistant index 0.5 were screened for antibacterial resistant genes (Tn3bla, GyrA, ParC, aadA2 and Sul1) using polymerase chain reaction.  Seventy (70) samples representing 65.4% were E. coli positive while fifty five (55) were resistant to at least one antibiotic. The highest level of resistance was against ampicillin (38.57% while the least was against cefalexin (4.29% Multidrug resistant isolates were 58.7% (41/70) while 28.6% (20/70) were resistant to three or more antibiotics. The result of the molecular characterization identified the five multidrug resistant isolates to be E. coli strain RAD34, E. coli strain CUSMBN2, E. coli strain CAU3471, and E. coli strain BYPFP. Tn3bla/GyrA/ParC resistant genes were detected in all the five isolates. E. coli isolate RAD34, E. coli strain CUSMBN2, E. coli strain CAU3471 and E. coli strain BYPFP possessed Sul1 gene. However, E. coli strain BYPFP, E. coli strain CAU3471 and E. coli strain CUSMBN2 had aadA2 gene.  The result of this study established the active circulation of Tn3bla, GyrA, ParC, aadA2 and Sul1 resistant genes among E. coli in the study area.
PCM Tool: Privacy Requirements Specification in Agile Software Development
Venue: Anais Estendidos da Conferencia Brasileira de Software: Teoria e Pratica
Year: 2019

Recent research has pointed out that software developers face difficulties to specify requirements for privacy sensitive systems. To help addressing this issue, this paper presents a tool, called PCM Tool, that supports the Privacy Criteria Method (PCM) an approach designed to guide the specification of privacy requirements in agile software development.
Evaluation and Analysis of the Profit Quality of Hainan's Listed Companies
Year: 2013

The evaluation and study of the profit quality can be used to analyze a company's financial position so as to provide a basis for its decision making.In this paper,an analysis is made of 23 listed companies of Hainan in 2010 in terms of their profit authenticity,profitability,cash earning,profit stability,growth and safety.As is found,the profit quality of the 23 companies is rather poor as a whole,with the profit status of some enterprises even being worrisome for the next fiscal year.
Enabling co creativity through digital storytelling in education
Year: 2018

This article asks how students interact in classroom activities that use digital storytelling to enable co creativity. The study is guided by a qualitative methodology and builds on five case studies in classes at lower and upper secondary level. Each case study consists of classroom activities that encompass one week, and 25 students; culminating at 119 lessons and 125 students. Classroom activities were facilitated by teachers or pre service teachers. Digital tools were used to engage students in digital storytelling. Data collection involved interviews and group discussions with students and teachers as well as field notes and videography of classroom activities. The Documentary Method was utilized to interpret research material and construct knowledge regarding students' interactions in classroom activities that aim for co creativity. Based on a constant comparison of the different case studies, common themes are established regarding how students interact in classroom activities that use digital storytelling to enable co creativity. Findings show two relevant phases of digital storytelling. In the digital story writing phase, students manifested two categories of co creativity engaged action and control. Students change different forms of engagement and immersive densities of engaged action through giving, taking, sharing or limiting control in the digital story writing activities. In the digital story producing phase, students experienced co creative flow as shared enjoyment and fun that emerged through full immersion in the digital story producing activities, in which control and rationality were absent.