nils-herrmann/clean_scirepeval huggingface dataset:
These aren't the kids I signed up for: the lived experience of general education, early childhood preservice teachers in classrooms for children with special needs
Year: 2020

Effective inclusive teaching practices continue to be an area of uncertainty for preservice and practicing teachers. This qualitative study examined the lived experiences of three, general education, early childhood, preservice teachers (PST) completing a field experience in preschool classrooms for children with significant disabilities. All three PSTs in the study were completing a semester long requisite field experience while concurrently completing an introduction to special education course. Both the field experience and the introduction to special education course were required for their early childhood, general education certification program. While the PSTs initially acknowledged anxiety related to working with children with significant disabilities, the levels of anxiety decreased during the experience. Additionally, PSTs noted the importance of the pedagogical skills they acquired from their special education mentor teachers. Highly skilled, special education mentor teachers were noted being critical to a successful experience.
A Roadmap to the Future of American Engineering Practice, Research, and Education
Year: 2009

We live in a time of great change, an increasingly global society, driven by the exponential growth of new knowledge and knitted together by rapidly evolving information and communication technologies. It is a time of challenge and contradiction, as an ever increasing human population threatens global sustainability; a global, knowledge driven economy places a new premium on technological workforce skills through phenomena such as outsourcing and offshoring; governments place increasing confidence in market forces to reflect public priorities, even as new paradigms such as open source software and open content knowledge and learning challenge conventional free market philosophies; and shifting geopolitical tensions are driven by the great disparity in wealth and power about the globe, manifested in the current threat to homeland security by terrorism. Yet it is also a time of unusual opportunity and optimism as new technologies not only improve the human condition but also enable the creation and flourishing of new communities and social institutions more capable of addressing the needs of our society.
The Empowering Leadership Project
Venue: Journal of Management Education
Year: 2019

The Empowering Leadership Project was developed by the authors to improve management students' practice and understanding of empowering leadership, an emerging theory for leadership in the team context that includes Power Sharing, Motivation Support, and Development Support. In the project, student teams select a short leadership challenge, developed from the authors' consulting work with mid level team leaders, that exemplifies one of the key aspects of empowering leadership. The teams conduct research on the leadership challenge, analyze research, and prepare proposed solutions and implementation plans to solve the leadership challenge. Instructions for the Empowering Leadership Project are provided as well as students' reactions to the project.
Thermal Infrared Pedestrian Segmentation Based on Conditional GAN
Venue: IEEE Transactions on Image Processing
Year: 2019

A novel thermal infrared pedestrian segmentation algorithm based on conditional generative adversarial network (IPS cGAN) is proposed for intelligent vehicular applications. The convolution backbone architecture of the generator is based on the improved U Net with residual blocks for well utilizing regional semantic information. Moreover, cross entropy loss for segmentation is introduced as the condition for the generator. SandwichNet, a novel convolutional network with symmetrical input, is proposed as the discriminator for real fake segmented images. Based on the c GAN framework, good segmentation performance could be achieved for thermal infrared pedestrians. Compared to some supervised and unsupervised segmentation algorithms, the proposed algorithm achieves higher accuracy with better robustness, especially for complex scenes.
Commentary on Bertagnolli et al. Leveraging electronic health record data for clinical trials a brave new world
Venue: Clinical trials
Year: 2020

Clinical trial research seemingly appears to be at the cusp of a paradigm shift. Big data and artificial intelligence. Real world data and real world evidence. Totality of evidence and lifecycle evaluation. These phrases are ubiquitous today. We hear them from pharmaceutical and medical device industry executives. They have been written into U.S. laws and regulations. They are used at scientific conferences, on government panels, and by consulting organizations, promising new ways to gain insights on medical product use and performance. And with their frequent use, and overuse, the clinical research community teeters between 'is there true promise that can bring actual progress? or 'is this nothing but puffery and hype? Today, we live in an age where online retailers are able to predict consumer spending through past queries and spending patterns, making iterative advances with every purchase or search. Yet at the same time, despite the increasing availability of health and clinical data from electronic health records (EHRs) to personal digital devices tracking sleep, activity, and diet and clear advances in statistical methods and computing capacity, we have not yet experienced the same transformational changes to clinical trial research. Should we not be able to use these 'real world data' to generate 'real world evidence' or increase the efficiency of clinical trials? We should. And there is likely to be actual promise and progress. In time, we will enter a brave new world of conducting clinical trials to evaluate medical product safety and effectiveness by taking advantage of realworld data sources. But we will need to learn how to appropriately use these data and understand the associated tradeoffs, not expect that real world data will be effortlessly substituted for traditional clinical trials and obviate tried and true methods of data collection. And to make progress, several steps are needed to establish standards and best practices. First, we need to agree on definitions. What are realworld data? Do they include patient reporting of satisfaction or adverse experiences on social media? Does a traditional randomized clinical trial that relies on an EHR, administrative claims or a disease registry for data collection count as 'real world' data? Realworld data have been defined by the U.S. Food and Drug Administration (FDA) as
Integrating estimation of distribution algorithms versus Q learning into Meta RaPS for solving the 0 1 multidimensional knapsack problem
Venue: Comput. Ind. Eng.
Year: 2017

Finding near optimal solutions in an acceptable amount of time is a challenge when developing sophisticated approximate approaches. A powerful answer to this challenge might be reached by incorporating intelligence into metaheuristics. We propose integrating two methods into Meta RaPS (Metaheuristic for Randomized Priority Search) which is currently classified as a memoryless metaheuristic. The first method is the Estimation of Distribution Algorithms (EDA) and the second is utilizing a machine learning algorithm known as Q Learning. To evaluate their performance, the proposed algorithms are tested on the 0 1 Multidimensional Knapsack Problem (MKP) Meta RaPS EDA appears to perform better than Meta RaPS Q Learning. However, both showed promising results compared to other approaches presented in the literature for the 0 1 MKP.
Dengue disease prediction using weka data mining tool
Venue: ArXiv
Year: 2015

Dengue is a life threatening disease prevalent in several developed as well as developing countries like India.In this paper we discuss various algorithm approaches of data mining that have been utilized for dengue disease prediction. Data mining is a well known technique used by health organizations for classification of diseases such as dengue, diabetes and cancer in bioinformatics research. In the proposed approach we have used WEKA with 10 cross validation to evaluate data and compare results. Weka has an extensive collection of different machine learning and data mining algorithms. In this paper we have firstly classified the dengue data set and then compared the different data mining techniques in weka through Explorer, knowledge flow and Experimenter interfaces. Furthermore in order to validate our approach we have used a dengue dataset with 108 instances but weka used 99 rows and 18 attributes to determine the prediction of disease and their accuracy using classifications of different algorithms to find out the best performance. The main objective of this paper is to classify data and assist the users in extracting useful information from data and easily identify a suitable algorithm for accurate predictive model from it. From the findings of this paper it can be concluded that Na\"ive Bayes and J48 are the best performance algorithms for classified accuracy because they achieved maximum accuracy= 100% with 99 correctly classified instances, maximum ROC 1, had least mean absolute error and it took minimum time for building this model through Explorer and Knowledge flow results
Development of the U.S. Cyber Security Strategy Legislation and the Enlightenment for China
Year: 2016

Cyber security strategies, cyber security legislation, mastery of the cyber.  Since 2011, the global arms race of cyber is intensifying. In order to realize the cyber deterrence and seek the strategic advantage, USA has issued some programmatic documents to promote the development of cyber security, including National Strategy for Trusted Identities in Cyberspace, International Strategy for Cyberspace, Action Strategy for Cyberspace, and National Strategy for Information Sharing and Safeguarding. From the Clinton administration to the Bush administration, and to the Obama administration, USA is gradually developing cyber security related aspects of organization control, network technology and military capabilities, aiming at taking further control over the power of setting the security standards and rules of international Internet in a new round of "network warfare" This work analyzes the development and characteristics of American cyberspace security strategy legislation and provides some enlightenment for our country.
Transforming food systems with agroecology
Year: 2016

Agroecology is a way of redesigning food systems, from the farm to the table, with a goal of achieving ecological, economic, and social sustainability. Through transdisciplinary, participatory, and change oriented research and action, agroecology links together science, practice, and movements focused on social change. But what are the steps that must be taken to transform food systems toward the ultimate goal of sustainability, and away from the mounting evidence of the negative impacts on the environment and society caused by modern industrial agriculture? In the latest edition of my agroecology textbook (Gliessman 2015b) I propose a framework for classifying "levels" of food system change. The first three levels describe the steps farmers can actually take on their farms for converting from industrial or conventional agroecosystems. Two additional levels go beyond the farm to the broader food system and the societies in which they are embedded. All five levels taken together can serve as a roadmap that outlines in an almost stepwise manner a process for transforming the entire global food system:  Increase the efficiency of industrial and conventional practices in order to reduce the use and consumption of costly, scarce, or environmentally damaging inputs. The primary goal of change at this level is to use industrial inputs more efficiently so that fewer inputs will be needed and the negative impacts of their use will also be reduced. Most conventional agricultural research has taken place at this level, through which considerable modern agricultural technologies, inputs, and practices have been developed. This research has helped farmers maintain or increase production through such practices as improved seeds, optimum planting density, more efficient pesticide and fertilizer application, and more precise use of water. So called "precision agriculture" is a recent focus of research at Level 1. Although this kind of research has reduced some of the negative impacts of industrial agriculture, they do not help break its dependence on external human inputs and monoculture practices.  Substitute alternative practices for industrial/conventional inputs and practices. The goal of this level of transition is to replace external input intensive and environmentally degrading products and practices with those that are more renewable, based on natural products, and more environmentally sound. Organic farming and biodynamic agriculture are examples of this approach. They employ alternative practices that include the use of nitrogen fixing covercrops and rotations to replace synthetic nitrogen fertilizers, the use of natural controls of pests and diseases, and the use of organic composts for fertility and soil organic matter AGROECOLOGY AND SUSTAINABLE FOOD SYSTEMS 2016, VOL. 40, NO. 3, 187 189 http:/dx.doi.org/10.1080/21683565.2015.1130765
1 1 2010 Developing High Performance III V Superlattice IRFPAs for Defense Challenges and Solutions
Year: 2016

The antimonide superlattice infrared detector technology program was established to explore new infrared detector materials and technology. The ultimate goal is to enhance the infrared sensor system capability and meet challenging requirements for many applications. Certain applications require large format focal plane arrays (FPAs) for a wide field of view. These FPAs must be able to detect infrared signatures at long wavelengths, at low infrared background radiation, and with minimal spatial cross talk. Other applications require medium format pixel, co registered, dual band capability with minimal spectral cross talk. Under the technology program, three leading research groups have focused on device architecture design, high quality material growth and characterization, detector and detector array processing, hybridization, testing, and modeling. Tremendous progress has been made in the past few years. This is reflected in orders of magnitude reduction in detector dark current density and substantial increase in quantum efficiency, as well as the demonstration of good quality long wavelength infrared FPAs. Many technical challenges must be overcome to realize the theoretical promise of superlattice infrared materials. These include further reduction in dark current density, growth of optically thick materials for high quantum efficiency, and elimination of FPA processing related performance degradation. In addition, challenges in long term research and development cost, superlattice material availability, FPA chip assembly availability, and industry sustainability are also to be met. A new program was established in 2009 with a scope that is different from the existing technology program. Called Fabrication of Superlattice Infrared FPA (FastFPA) this 4 year program sets its goal to establish U.S. industry capability of producing high quality superlattice wafers and fabricating advanced FPAs. It uses horizontal integration strategy by leveraging existing III V industry resources and taking advantage of years of valuable experiences amassed by the HgCdTe FPA industry. By end of the program span, three sets of FPAs will be demonstrated a small format long wave FPA, a large format long wave FPA, and a medium format dual band FPA at long wave and mid wave infrared.
Academic writing, scholarly identity, voice and the benefits and challenges of multilingualism: Reflections from Norwegian doctoral researchers in teacher education
Year: 2020

Doctoral researchers increasingly write in English where English is a non ambient language, for example, in Norway. Yet, similar to other contexts, a goal of the Norwegian doctoral degree is that doctoral graduates are able to communicate their research in both national and international contexts, which usually means English. Through narrative analysis of 17 responses from doctoral researchers to a prompt asking about their journeys into academic writing, this article explores perceptions of how multilingualism and academic writing intersect with the emerging identities and voices of doctoral researchers as researchers. In these written narratives, doctoral researchers reflected upon previous experience and academia; audience and choice of language; voice, academic writing, and the perception of linguistic deficit; and academic writing conventions and language. While much previous research has focused on the negative aspects of multilingual and professional identities as academic writers, our narratives reveal how some doctoral researchers use their experiences to create productive strategies and resources. One key finding is that doctoral researchers create virtual transnational communities through their multilingual academic writing, both in terms of academic publishing and social media platforms.
Stereo magnification
Venue: ACM Trans. Graph.
Year: 2018

The view synthesis problem generating novel views of a scene from known imagery has garnered recent attention due in part to compelling applications in virtual and augmented reality. In this paper, we explore an intriguing scenario for view synthesis: extrapolating views from imagery captured by narrow baseline stereo cameras, including VR cameras and now widespread dual lens camera phones. We call this problem stereo magnification, and propose a learning framework that leverages a new layered representation that we call multiplane images (MPIs) Our method also uses a massive new data source for learning view extrapolation: online videos on YouTube. Using data mined from such videos, we train a deep network that predicts an MPI from an input stereo image pair. This inferred MPI can then be used to synthesize a range of novel views of the scene, including views that extrapolate significantly beyond the input baseline. We show that our method compares favorably with several recent view synthesis methods, and demonstrate applications in magnifying narrow baseline stereo images.
Photocatalyzed degradation/abatement of endocrine disruptors
Year: 2017

Endocrine disrupting chemicals (EDCs) can cause adverse health effects in reproductive, neurological and immune systems, both on wildlife and on humans. EDCs may enter the environment from a variety of sources: run off from agricultural lands, leakage from septic tanks, sewage treatment plants (STP) Despite are usually present at trace concentrations, EDCs can still cause significant serious damage. Worldwide EDCs release constantly increases, and it has been demonstrated that the efficiency of conventional water treatment technologies against these contaminants is very limited. Advanced oxidation processes (AOPs) constitute a promising technology for removal of most persistent organic pollutants (POPs) such as EDCs, from waters, and among AOPs, photocatalytic methods are likely the most promising, specially those involving Vis light responsive materials, i.e solar heterogeneous photocatalysis. This review provides a general and critical overview of the most recent state of the art research on the photocatalytic degradation of EDCs over the last two years. We start describing briefly the problems posed by EDCs in the environment and the fundamentals of photocatalysis, and then focus on the degradation of EDCs in the presence of photocatalysts under irradiation.
Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts
Venue: NeurIPS
Year: 2020

By transferring both features and gradients between different layers, shortcut connections explored by ResNets allow us to effectively train very deep neural networks up to hundreds of layers. However, the additional computation costs induced by those shortcuts are often overlooked. For example, during online inference, the shortcuts in ResNet 50 account for about 40 percent of the entire memory usage on feature maps, because the features in the preceding layers cannot be released until the subsequent calculation is completed. In this work, for the first time, we consider training the CNN models with shortcuts and deploying them without. In particular, we propose a novel joint training framework to train plain CNN by leveraging the gradients of the ResNet counterpart. During forward step, the feature maps of the early stages of plain CNN are passed through later stages of both itself and the ResNet counterpart to calculate the loss. During backpropagation, gradients calculated from a mixture of these two parts are used to update the plainCNN network to solve the gradient vanishing problem. Extensive experiments on ImageNet/CIFAR10/CIFAR100 demonstrate that the plainCNN network without shortcuts generated by our approach can achieve the same level of accuracy as that of the ResNet baseline while achieving about 1.4x speed up and 1.25x memory reduction. We also verified the feature transferability of our ImageNet pretrained plain CNN network by fine tuning it on MIT 67 and Caltech 101. Our results show that the performance of the plain CNN is slightly higher than that of its baseline ResNet 50 on these two datasets. The code will be available at https:/github.com/leoozy/JointRD_Neurips2020 and the MindSpore code will be available at https:/www.mindspore.cn/resources/hub.
Smart Contract enabled Online Examination System Based in Blockchain Network
Venue: 2021 International Conference on Computer Communication and Informatics (ICCCI)
Year: 2021

In today's world, data is one of the most important assets than any other. Every user wants to secure their data from the outer world. Blockchain is the prominent technology that can provide the security and loyalty of data. Initially, blockchain has been used for the cryptocurrency and all the data were available on the public distributed ledger. But now a days, private blockchain is widely used within the organizations for data security. Blockchain engenders decentralized systems in which data can be send and receive securely and efficiently over the network. It means there is everything is hidden from the outer world; only authorized users have the authority to read and write the data on the network. World's topmost industries; like Walmart, IBM, Google, etc. are adopting the blockchain technology to build the Decentralized Applications (DApps) Decentralized Applications are the smart systems that are executed on a distributed computer network. Blockchain enables one of the most secure applications called Smart Contract. Smart Contracts are the computerized and secured distributed ledgers that enable secure, transparent, and tamper proof transactions. Smart contracts create and verify the data with the help of hashing. It is a mathematical procedure that uses the most powerful algorithm cryptographic Hash Algorithm i.e. SHA 256. It engenders 256 bit signature for the input text.  Blockchain Platform is a widely used platform to build the DApps. This platform is a public network platform, which is open to all and anyone can participate in this network to send and receives the transactions. Blockchain Technology is enabled in every sector like marketing, business, education and supply chain, etc. This paper carries out the study Ethereum Blockchain Platform in Education System. We have developed an application for the Online Examination System using Blockchain Ethereum Platform with features of Smart Contracts that enables server runtime environment NodeJS and MongoDB database system. Blockchain based system is truly more secure than all the Cloud based systems. We have also analysed that how blockchain based online examination is more trustworthy as compare the other systems.