nils-herrmann/clean_scirepeval huggingface dataset:
Sleep deprivation and clinical performance.

PHYSICIANS’ ABILITY TO PROVIDE high-quality care can be adversely affected by many factors, including sleep deprivation. Concerns about the danger of physicians who are sleep deprived and providing care have led state legislatures and academic institutions to try to constrain the work hours of physicians in training (house staff). Unlike commercial aviation, for example, medicine is an industry in which public safety is directly at risk but does not have mandatory restrictions on work hours. Legislation before the US Congress calls for limiting resident work hours to 80 hours per week and no more than 24 hours of continuous work. Shifts of residents working in the emergency department would be limited to 12 hours. The proposed legislation, which includes public disclosure and civil penalties for hospitals that violate the work hour restrictions, does not address extended duty shifts of attending or private practice physicians. There is still substantial controversy within the medical community about the magnitude and significance of the clinical impairment resulting from work schedules that aggravate sleep deprivation. There is extensive literature on the adverse effects of sleep deprivation in laboratory and nonmedical settings. However, studies on sleep deprivation of physicians performing clinically relevant tasks have been less conclusive. Opinions have been further influenced by the potential adverse impact of reduced work schedules on the economics of health care, on continuity of care, and on quality of care. This review focuses on the consequences of sleep loss both in controlled laboratory environments and in clinical studies involving medical personnel.
mTOR Signaling in Growth, Metabolism, and Disease

The mechanistic target of rapamycin (mTOR) coordinates eukaryotic cell growth and metabolism with environmental inputs, including nutrients and growth factors. Extensive research over the past two decades has established a central role for mTOR in regulating many fundamental cell processes, from protein synthesis to autophagy, and deregulated mTOR signaling is implicated in the progression of cancer and diabetes, as well as the aging process. Here, we review recent advances in our understanding of mTOR function, regulation, and importance in mammalian physiology. We also highlight how the mTOR signaling network contributes to human disease and discuss the current and future prospects for therapeutically targeting mTOR in the clinic.
A Review of Relation Classification with Convolutional Neural Network

Relation classification is one of the important research issues in the field of Natural Language Processing (NLP). It is a crucial intermediate step in complex knowledge intensive applications like automatic knowledgebase construction, question answering, textual entailment, search engine etc. Recently neural network has given state of art results in various relation extraction tasks without depending much on manually engineered features. In this paper we present brief review on different model that has been proposed for relation classification and compare their results.
Evaluating ensemble classifiers for spam filtering

In this study, the ensemble classifier presented by Caruana, Niculescu-Mizil, Crew & Ksikes (2004) is investigated. Their ensemble approach generates thousands of models using a variety of machine learning algorithms and uses a forward stepwise selection to build robust ensembles that can be optimised to an arbitrary metric. On average, the resulting ensemble out-performs the best individual machine learning models. The classifier is implemented in the WEKA machine learning environment, which allows the results presented by the original paper to be validated and the classifier to be extended to multi-class problem domains. The behaviour of different ensemble building strategies is also investigated. The classifier is then applied to the spam filtering domain, where it is tested on three different corpora in an attempt to provide a realistic evaluation of the system. It records similar performance levels to that seen in other problem domains and out-performs individual models and the naive Bayesian filtering technique regularly used by commercial spam filtering solutions. Caruana et al.’s (2004) classifier will typically outperform the best known models in a variety of problems.
Learning Deep Networks from Noisy Labels with Dropout Regularization

Large datasets often have unreliable labels—such as those obtained from Amazon's Mechanical Turk or social media platforms—and classifiers trained on mislabeled datasets often exhibit poor performance. We present a simple, effective technique for accounting for label noise when training deep neural networks. We augment a standard deep network with a softmax layer that models the label noise statistics. Then, we train the deep network and noise model jointly via end-to-end stochastic gradient descent on the (perhaps mislabeled) dataset. The augmented model is underdetermined, so in order to encourage the learning of a non-trivial noise model, we apply dropout regularization to the weights of the noise model during training. Numerical experiments on noisy versions of the CIFAR-10 and MNIST datasets show that the proposed dropout technique outperforms state-of-the-art methods.
Drizzle: Fast and Adaptable Stream Processing at Scale

Large scale streaming systems aim to provide high throughput and low latency. They are often used to run mission-critical applications, and must be available 24x7. Thus such systems need to adapt to failures and inherent changes in workloads, with minimal impact on latency and throughput. Unfortunately, existing solutions require operators to choose between achieving low latency during normal operation and incurring minimal impact during adaptation. Continuous operator streaming systems, such as Naiad and Flink, provide low latency during normal execution but incur high overheads during adaptation (e.g., recovery), while micro-batch systems, such as Spark Streaming and FlumeJava, adapt rapidly at the cost of high latency during normal operations.
 Our key observation is that while streaming workloads require millisecond-level processing, workload and cluster properties change less frequently. Based on this, we develop Drizzle, a system that decouples the processing interval from the coordination interval used for fault tolerance and adaptability. Our experiments on a 128 node EC2 cluster show that on the Yahoo Streaming Benchmark, Drizzle can achieve end-to-end record processing latencies of less than 100ms and can get 2-3x lower latency than Spark. Drizzle also exhibits better adaptability, and can recover from failures 4x faster than Flink while having up to 13x lower latency during recovery.
What's new with the amnesic patient H.M.?

H.M. became amnesic in 1953. Since that time, nearly 100 investigators, first at the Montreal Neurological Institute and since 1966 at the Massachusetts Institute of Technology, have participated in studying him. We all understand the rare opportunity we have had to work with him, and we are grateful for his dedication to research. He has taught us a great deal about the cognitive and neural organization of memory. We are in his debt.
SlimNets: An Exploration of Deep Model Compression and Acceleration

Deep neural networks have achieved increasingly accurate results on a wide variety of complex tasks. However, much of this improvement is due to the growing use and availability of computational resources (e.g use of GPUs, more layers, more parameters, etc). Most state-of-the-art deep networks, despite performing well, over-parameterize approximate functions and take a significant amount of time to train. With increased focus on deploying deep neural networks on resource constrained devices like smart phones, there has been a push to evaluate why these models are so resource hungry and how they can be made more efficient. This work evaluates and compares three distinct methods for deep model compression and acceleration: weight pruning, low rank factorization, and knowledge distillation. Comparisons on VGG nets trained on CIFAR10 show that each of the models on their own are effective, but that the true power lies in combining them. We show that by combining pruning and knowledge distillation methods we can create a compressed network 85 times smaller than the original, all while retaining 96% of the original model's accuracy.
A Brief Review of Network Embedding

Learning the representations of nodes in a network can benefit various analysis tasks such as node classification, link prediction, clustering, and anomaly detection. Such a representation learning problem is referred to as network embedding, and it has attracted significant attention in recent years. In this article, we briefly review the existing network embedding methods by two taxonomies. The technical taxonomy focuses on the specific techniques used and divides the existing network embedding methods into two stages, i.e., context construction and objective design. The non-technical taxonomy focuses on the problem setting aspect and categorizes existing work based on whether to preserve special network properties, to consider special network types, or to incorporate additional inputs. Finally, we summarize the main findings based on the two taxonomies, analyze their usefulness, and discuss future directions in this area.
A Bayesian analysis of the multinomial probit model using marginal data augmentation

We introduce a set of new Markov chain Monte Carlo algorithms for Bayesian analysis of the multinomial probit model. Our Bayesian representation of the model places a new, and possibly improper, prior distribution directly on the identi0able parameters and thus is relatively easy to interpret and use. Our algorithms, which are based on the method of marginal data augmentation, involve only draws from standard distributions and dominate other available Bayesian methods in that they are as quick to converge as the fastest methods but with a more attractive prior speci0cation. C-code along with an R interface for our algorithms is publicly available.
Hiding data in images by simple LSB substitution

In this paper, a data hiding scheme by simple LSB substitution is proposed. By applying an optimal pixel adjustment process to the stego-image obtained by the simple LSB substitution method, the image quality of the stego-image can be greatly improved with low extra computational complexity. The worst case mean-square-error between the stego-image and the cover-image is derived. Experimental results show that the stego-image is visually indistinguishable from the original cover-image. The obtained results also show a signi7cant improvement with respect to a previous work. ? 2003 Pattern Recognition Society.
Temperature monitored IoT based smart incubator

Baby neonatal Incubator is a closed apparatus for providing a controlled environment in all the possible ways for the immense care of premature babies. Majority of premature babies are born between 32 to 37 weeks of gestation period and die due to lack of simple essential care such as warmth. Until recently, most of the developing countries turned a blind eye to premature babies leading to their demise. This research work provides a cost-worthy design of an embedded device for real time monitoring of newborn babies in the incubator. It permits early detection of potential life threatening events and maintains a safe environment for the infant. Many of the existing medical technological companies (such as small and medium) may not adopt the best existing technologies as its maintenance might not be cost effective. On the other hand the bigger medical firms which have adopted them are cost effective and common man cannot afford the same. So, the main objective of this research is to overcome the drawbacks and provide eco-friendly service to all the common people.
Semiparallel deep neural network hybrid architecture: first application on depth from monocular camera

Publication Information Bazrafkan, Shabab , Javidnia, Hossein , Lemley, Joseph , & Corcoran, Peter (2018). Semiparallel deep neural network hybrid architecture: first application on depth from monocular camera. Journal of Electronic Imaging, 27(4), 19. doi: 10.1117/1.JEI.27.4.043041 Publisher Society of Photo-optical Instrumentation Engineers (SPIE) Link to publisher's version
Customer relationship management classification using data mining techniques

Customer Relationship Management possess Business Intelligence by incorporating information acquisition, information storage, and decision support functions to provide customized customer service. It enables customer representatives to analyze and classify data to address customer needs in order to promote greater customer satisfaction and retention, but in reality we have learned CRM classification models are outdated, substandard because of noisy and unbalanced data set. In this paper, a new feature selection method is proposed to resolve such CRM data set with relevant features by incorporating an efficient data mining techniques to improve data quality and feature relevancy after preprocessing. Finally it enhances the performance of classification.
Contextual learning: a reflective learning intervention for nursing education.

Nursing educators need to continue to explore ways that new pedagogies such as narrative pedagogy and reflective practice inform and extend students' thinking in classroom and clinical situations. The goal of instruction becomes creating an opportunity for learning that integrates content knowledge with knowledge of the context. Educational methodologies that incorporate the use of context in a reflective, dialogical approach over time hold much promise in developing a dynamic process of thinking in practice. Contextual learning is a reflective learning intervention that offers new possibilities for nurse educators to prepare nurses to think critically in practice. In this expository paper the design and instructional methodology of contextual learning is discussed, beginning with a brief overview of the nature of critical thinking and the use of narrative as major underpinnings in the development of this intervention. Examples of how the intervention was implemented with novice nurses in practice is provided. Finally, reflections on how the intervention could be refined for nursing students is offered.
Towards hybrid NER : an extended study of content and crowdsourcing-related performance factors

This paper explores the factors that influence the human component in hybrid approaches to named entity recognition (NER) in microblogs, which combine state-of-the-art automatic techniques with human and crowd computing. We identify a set of content and crowdsourcing-related features (number of entities in a post, types of entities, content sentiment, skipped truepositive posts, average time spent to complete the tasks, and interaction with the user interface) and analyse their impact on the accuracy of the results and the timeliness of their delivery. Using CrowdFlower and a simple, custom built gamified NER tool we run experiments on three datasets from related literature and a fourth newly annotated corpus. Our findings show that crowd workers are adept at recognizing people, locations, and implicitly identified entities within shorter microposts. We expect these findings to lead to the design of more advanced NER pipelines, informing the way in which tweets are chosen to be outsourced or processed by automatic tools. Experimental results are published as JSON-LD for further use by the research community.
Automatic Documentation of ICD Codes with Far-Field Speech Recognition

Documentation errors increase healthcare costs and cause unnecessary patient deaths. As the standard language for diagnoses and billing, ICD codes serve as the foundation for medical documentation worldwide. Despite the prevalence of electronic medical records, hospitals still witness high levels of ICD miscoding. In this paper, we propose to automatically document ICD codes with far-field speech recognition. Far-field speech occurs when the microphone is located several meters from the source, as is common with smart homes and security systems. Our method combines acoustic signal processing with recurrent neural networks to recognize and document ICD codes in real time. To evaluate our model, we collected a far-field speech dataset of ICD-10 codes and found our model to achieve 87% accuracy with a BLEU score of 85%. By sampling from an unsupervised medical language model, our method is able to outperform existing methods. Overall, this work shows the potential of automatic speech recognition to provide efficient, accurate, and cost-effective healthcare documentation.
Integrating artifical intelligence with anylogic simulation

Simulation is one of five key technologies that PwC's Artificial Intelligence Accelerator lab uses to build Artificial Intelligence (AI) applications. Application of AI is accelerating rapidly, spawning new sectors, and resulting in unprecedented reach, power, and influence. Simulation explicitly captures the behavior of agents and processes that can either be described by or replaced by AI components. AI components can be embedded into a simulation to provide learning or adaptive behavior. And, simulation can be used to evaluate the impact of introducing AI into a “real world system” such as supply chains or production processes. In this workshop we will demonstrate an Agent-Based Model with Reinforcement Learning for Autonomous Fleet Coordination; demonstrate and describe in detail a version of the AnyLogic Consumer Market Model that has been modified to include adaptive dynamics based on deep learning; and describe approaches to integrating machine learning to the design and development of simulations.
Optimizing the Latent Space of Generative Networks

Generative Adversarial Networks (GANs) have achieved remarkable results in the task of generating realistic natural images. In most successful applications, GAN models share two common aspects: solving a challenging saddle point optimization problem, interpreted as an adversarial game between a generator and a discriminator functions; and parameterizing the generator and the discriminator as deep convolutional neural networks. The goal of this paper is to disentangle the contribution of these two factors to the success of GANs. In particular, we introduce Generative Latent Optimization (GLO), a framework to train deep convolutional generators using simple reconstruction losses. Throughout a variety of experiments, we show that GLO enjoys many of the desirable properties of GANs: synthesizing visually-appealing samples, interpolating meaningfully between samples, and performing linear arithmetic with noise vectors; all of this without the adversarial optimization scheme.
How an Australian Retailer Enabled Business Transformation Through Enterprise Architecture

1 Sia Siew Kien, Michael Rosemann and Phillip Yetton are the accepting senior editors for this article. 2 This research was partly funded by an Australian Research Council Discovery grant. The authors are grateful to the interviewees, whose willingness to share their valuable insights and experiences made this study possible, and to the senior editors and reviewers for their very helpful feedback and advice throughout the review process. 3 All quotes in this article are from employees of “RetailCo,” the subject of this case study. The names of the organization and its business divisions have been anonymized. 4 A digital business platform is “an integrated set of electronic business processes and the technologies, applications and data supporting those processes” Weill, P. and Ross, J. W. IT Savvy: What Top Executives Must Know to Go from Pain to Gain, Harvard Business School Publishing, 2009, p. 4; for more on digitized platforms, see pp. 67-87 of this publication.
Digital Signature

There are different types of encryption techniques are being used to ensure the privacy of data transmitted over internet. Digital Signature is a mathematical scheme which ensures the privacy of conversation, integrity of data, authenticity of digital message/sender and non-repudiation of sender. Digital Signature is embedded in some hardware device or also exits as a file on a storage device. Digital Signature are signed by third party some certifying authority. This paper describe the different key factor of digital signature with the working of digital signature, through various methods and procedures involved in signing the data or message by using digital signature. It introduces algorithms used in digital signatures.
ALE: Additive Latent Effect Models for Grade Prediction

The past decade has seen a growth in the development and deployment of educational technologies for assisting college-going students in choosing majors, selecting courses and acquiring feedback based on past academic performance. Grade prediction methods seek to estimate a grade that a student may achieve in a course that she may take in the future (e.g., next term). Accurate and timely prediction of students’ academic grades is important for developing effective degree planners and early warning systems, and ultimately improving educational outcomes. Existing grade prediction methods mostly focus on modeling the knowledge components associated with each course and student, and often overlook other factors such as the difficulty of each knowledge component, course instructors, student interest, capabilities and effort. In this paper, we propose additive latent effect models that incorporate these factors to predict the student next-term grades. Specifically, the proposed models take into account four factors: (i) student’s academic level, (ii) course instructors, (iii) student global latent factor, and (iv) latent knowledge factors. We compared the new models with several state-of-the-art methods on students of various characteristics (e.g., whether a student transferred in or not). The experimental results demonstrate that the proposed methods significantly outperform the baselines on grade prediction problem. Moreover, we perform a thorough analysis on the importance of different factors and how these factors can practically assist students in course selection, and finally improve their academic performance.
Skin lesion classification with ensemble of squeeze-and-excitation networks and semi-supervised learning

In this report, we introduce the outline of our system in Task 3: Disease Classification of ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection. We fine-tuned multiple pre-trained neural network models based on Squeeze-and-Excitation Networks (SENet) which achieved state-of-the-art results in the field of image recognition. In addition, we used the mean teachers as a semi-supervised learning framework and introduced some specially designed data augmentation strategies for skin lesion analysis. We confirmed our data augmentation strategy improved classification performance and demonstrated 87.2% in balanced accuracy on the official ISIC2018 validation dataset.
Computer vision based method for real-time fire and flame detection q

This paper proposes a novel method to detect fire and/or flames in real-time by processing the video data generated by an ordinary camera monitoring a scene. In addition to ordinary motion and color clues, flame and fire flicker is detected by analyzing the video in the wavelet domain. Quasi-periodic behavior in flame boundaries is detected by performing temporal wavelet transform. Color variations in flame regions are detected by computing the spatial wavelet transform of moving fire-colored regions. Another clue used in the fire detection algorithm is the irregularity of the boundary of the fire-colored region. All of the above clues are combined to reach a final decision. Experimental results show that the proposed method is very successful in detecting fire and/or flames. In addition, it drastically reduces the false alarms issued to ordinary fire-colored moving objects as compared to the methods using only motion and color clues.